Instruction and logic for a vector format for processing computations

ABSTRACT

A processor includes a front end to fetch an instruction. The instruction is to calculate a data point using inputs from a plurality of adjacent source data in a plurality of dimensions. The processor includes a decoder to decode the instruction. The processor also includes a core to, based on the decoded instruction, perform a plurality of tabular vector read operations to read the plurality of adjacent source data and perform a tabular vector calculation to execute the instruction. The tabular vector calculation is based upon results of performing the plurality of tabular vector read operations. The core is further to write results of the tabular vector calculation.

FIELD OF THE INVENTION

The present disclosure pertains to the field of processing logic,microprocessors, and associated instruction set architecture that, whenexecuted by the processor or other processing logic, perform logical,mathematical, or other functional operations.

DESCRIPTION OF RELATED ART

Multiprocessor systems are becoming more and more common. Applicationsof multiprocessor systems include dynamic domain partitioning all theway down to desktop computing. In order to take advantage ofmultiprocessor systems, code to be executed may be separated intomultiple threads for execution by various processing entities. Eachthread may be executed in parallel with one another. Furthermore, inorder to increase the utility of a processing entity, out-of-orderexecution may be employed. Out-of-order execution may executeinstructions as input to such instructions is made available. Thus, aninstruction that appears later in a code sequence may be executed beforean instruction appearing earlier in a code sequence.

DESCRIPTION OF THE FIGURES

Embodiments are illustrated by way of example and not limitation in theFigures of the accompanying drawings:

FIG. 1A is a block diagram of an exemplary computer system formed with aprocessor that may include execution units to execute an instruction, inaccordance with embodiments of the present disclosure;

FIG. 1B illustrates a data processing system, in accordance withembodiments of the present disclosure;

FIG. 1C illustrates other embodiments of a data processing system forperforming text string comparison operations;

FIG. 2 is a block diagram of the micro-architecture for a processor thatmay include logic circuits to perform instructions, in accordance withembodiments of the present disclosure;

FIG. 3A illustrates various packed data type representations inmultimedia registers, in accordance with embodiments of the presentdisclosure;

FIG. 3B illustrates possible in-register data storage formats, inaccordance with embodiments of the present disclosure;

FIG. 3C illustrates various signed and unsigned packed data typerepresentations in multimedia registers, in accordance with embodimentsof the present disclosure;

FIG. 3D illustrates an embodiment of an operation encoding format;

FIG. 3E illustrates another possible operation encoding format havingforty or more bits, in accordance with embodiments of the presentdisclosure;

FIG. 3F illustrates yet another possible operation encoding format, inaccordance with embodiments of the present disclosure;

FIG. 4A is a block diagram illustrating an in-order pipeline and aregister renaming stage, out-of-order issue/execution pipeline, inaccordance with embodiments of the present disclosure;

FIG. 4B is a block diagram illustrating an in-order architecture coreand a register renaming logic, out-of-order issue/execution logic to beincluded in a processor, in accordance with embodiments of the presentdisclosure;

FIG. 5A is a block diagram of a processor, in accordance withembodiments of the present disclosure;

FIG. 5B is a block diagram of an example implementation of a core, inaccordance with embodiments of the present disclosure;

FIG. 6 is a block diagram of a system, in accordance with embodiments ofthe present disclosure;

FIG. 7 is a block diagram of a second system, in accordance withembodiments of the present disclosure;

FIG. 8 is a block diagram of a third system in accordance withembodiments of the present disclosure;

FIG. 9 is a block diagram of a system-on-a-chip, in accordance withembodiments of the present disclosure;

FIG. 10 illustrates a processor containing a central processing unit anda graphics processing unit which may perform at least one instruction,in accordance with embodiments of the present disclosure;

FIG. 11 is a block diagram illustrating the development of IP cores, inaccordance with embodiments of the present disclosure;

FIG. 12 illustrates how an instruction of a first type may be emulatedby a processor of a different type, in accordance with embodiments ofthe present disclosure;

FIG. 13 illustrates a block diagram contrasting the use of a softwareinstruction converter to convert binary instructions in a sourceinstruction set to binary instructions in a target instruction set, inaccordance with embodiments of the present disclosure;

FIG. 14 is a block diagram of an instruction set architecture of aprocessor, in accordance with embodiments of the present disclosure;

FIG. 15 is a more detailed block diagram of an instruction setarchitecture of a processor, in accordance with embodiments of thepresent disclosure;

FIG. 16 is a block diagram of an execution pipeline for an instructionset architecture of a processor, in accordance with embodiments of thepresent disclosure;

FIG. 17 is a block diagram of an electronic device for utilizing aprocessor, in accordance with embodiments of the present disclosure;

FIG. 18 is a block diagram of an example embodiment of a system 1800 fora vector format for processing computations, in accordance withembodiments of the present disclosure;

FIG. 19 is an illustration of example finite difference functions, inaccordance with embodiments of the present disclosure;

FIG. 20 is an illustration of example operation of a finite differencefunction, in accordance with embodiments of the present disclosure;

FIG. 21 is an illustration of example operation of an anisotropicfunction, in accordance with embodiments of the present disclosure;

FIG. 22 is an illustration of example operation of a system to makecalculations based upon tabular vector reads, in accordance withembodiments of the present disclosure; and

FIG. 23 is a flowchart of an example embodiment of a method for applyinga vector format for processing computations, in accordance withembodiments of the present disclosure.

DETAILED DESCRIPTION

The following description describes an instruction and processing logicfor a vector format for processing computations. In one embodiment, sucha format may include a tabular format. In another embodiment, suchcomputations may include finite-difference computations. For example,the computations may include differential equation calculations orestimations, multi-dimensional isotropic functions, or anisotropicfunctions. It will be appreciated, however, by one skilled in the artthat the embodiments may be practiced without such specific details.Additionally, some well-known structures, circuits, and the like havenot been shown in detail to avoid unnecessarily obscuring embodiments ofthe present disclosure.

Although the following embodiments are described with reference to aprocessor, other embodiments are applicable to other types of integratedcircuits and logic devices. Similar techniques and teachings ofembodiments of the present disclosure may be applied to other types ofcircuits or semiconductor devices that may benefit from higher pipelinethroughput and improved performance. The teachings of embodiments of thepresent disclosure are applicable to any processor or machine thatperforms data manipulations. However, the embodiments are not limited toprocessors or machines that perform 512-bit, 256-bit, 128-bit, 64-bit,32-bit, or 16-bit data operations and may be applied to any processorand machine in which manipulation or management of data may beperformed. In addition, the following description provides examples, andthe accompanying drawings show various examples for the purposes ofillustration. However, these examples should not be construed in alimiting sense as they are merely intended to provide examples ofembodiments of the present disclosure rather than to provide anexhaustive list of all possible implementations of embodiments of thepresent disclosure.

Although the below examples describe instruction handling anddistribution in the context of execution units and logic circuits, otherembodiments of the present disclosure may be accomplished by way of adata or instructions stored on a machine-readable, tangible medium,which when performed by a machine cause the machine to perform functionsconsistent with at least one embodiment of the disclosure. In oneembodiment, functions associated with embodiments of the presentdisclosure are embodied in machine-executable instructions. Theinstructions may be used to cause a general-purpose or special-purposeprocessor that may be programmed with the instructions to perform thesteps of the present disclosure. Embodiments of the present disclosuremay be provided as a computer program product or software which mayinclude a machine or computer-readable medium having stored thereoninstructions which may be used to program a computer (or otherelectronic devices) to perform one or more operations according toembodiments of the present disclosure. Furthermore, steps of embodimentsof the present disclosure might be performed by specific hardwarecomponents that contain fixed-function logic for performing the steps,or by any combination of programmed computer components andfixed-function hardware components.

Instructions used to program logic to perform embodiments of the presentdisclosure may be stored within a memory in the system, such as DRAM,cache, flash memory, or other storage. Furthermore, the instructions maybe distributed via a network or by way of other computer-readable media.Thus a machine-readable medium may include any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer), but is not limited to, floppy diskettes, optical disks,Compact Disc, Read-Only Memory (CD-ROMs), and magneto-optical disks,Read-Only Memory (ROMs), Random Access Memory (RAM), ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), magnetic or optical cards, flashmemory, or a tangible, machine-readable storage used in the transmissionof information over the Internet via electrical, optical, acoustical orother forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.). Accordingly, the computer-readablemedium may include any type of tangible machine-readable medium suitablefor storing or transmitting electronic instructions or information in aform readable by a machine (e.g., a computer).

A design may go through various stages, from creation to simulation tofabrication. Data representing a design may represent the design in anumber of manners. First, as may be useful in simulations, the hardwaremay be represented using a hardware description language or anotherfunctional description language. Additionally, a circuit level modelwith logic and/or transistor gates may be produced at some stages of thedesign process. Furthermore, designs, at some stage, may reach a levelof data representing the physical placement of various devices in thehardware model. In cases wherein some semiconductor fabricationtechniques are used, the data representing the hardware model may be thedata specifying the presence or absence of various features on differentmask layers for masks used to produce the integrated circuit. In anyrepresentation of the design, the data may be stored in any form of amachine-readable medium. A memory or a magnetic or optical storage suchas a disc may be the machine-readable medium to store informationtransmitted via optical or electrical wave modulated or otherwisegenerated to transmit such information. When an electrical carrier waveindicating or carrying the code or design is transmitted, to the extentthat copying, buffering, or retransmission of the electrical signal isperformed, a new copy may be made. Thus, a communication provider or anetwork provider may store on a tangible, machine-readable medium, atleast temporarily, an article, such as information encoded into acarrier wave, embodying techniques of embodiments of the presentdisclosure.

In modern processors, a number of different execution units may be usedto process and execute a variety of code and instructions. Someinstructions may be quicker to complete while others may take a numberof clock cycles to complete. The faster the throughput of instructions,the better the overall performance of the processor. Thus it would beadvantageous to have as many instructions execute as fast as possible.However, there may be certain instructions that have greater complexityand require more in terms of execution time and processor resources,such as floating point instructions, load/store operations, data moves,etc.

As more computer systems are used in internet, text, and multimediaapplications, additional processor support has been introduced overtime. In one embodiment, an instruction set may be associated with oneor more computer architectures, including data types, instructions,register architecture, addressing modes, memory architecture, interruptand exception handling, and external input and output (I/O).

In one embodiment, the instruction set architecture (ISA) may beimplemented by one or more micro-architectures, which may includeprocessor logic and circuits used to implement one or more instructionsets. Accordingly, processors with different micro-architectures mayshare at least a portion of a common instruction set. For example,Intel® Pentium 4 processors, Intel® Core™ processors, and processorsfrom Advanced Micro Devices, Inc. of Sunnyvale Calif. implement nearlyidentical versions of the x86 instruction set (with some extensions thathave been added with newer versions), but have different internaldesigns. Similarly, processors designed by other processor developmentcompanies, such as ARM Holdings, Ltd., MIPS, or their licensees oradopters, may share at least a portion a common instruction set, but mayinclude different processor designs. For example, the same registerarchitecture of the ISA may be implemented in different ways indifferent micro-architectures using new or well-known techniques,including dedicated physical registers, one or more dynamicallyallocated physical registers using a register renaming mechanism (e.g.,the use of a Register Alias Table (RAT), a Reorder Buffer (ROB) and aretirement register file. In one embodiment, registers may include oneor more registers, register architectures, register files, or otherregister sets that may or may not be addressable by a softwareprogrammer.

An instruction may include one or more instruction formats. In oneembodiment, an instruction format may indicate various fields (number ofbits, location of bits, etc.) to specify, among other things, theoperation to be performed and the operands on which that operation willbe performed. In a further embodiment, some instruction formats may befurther defined by instruction templates (or sub-formats). For example,the instruction templates of a given instruction format may be definedto have different subsets of the instruction format's fields and/ordefined to have a given field interpreted differently. In oneembodiment, an instruction may be expressed using an instruction format(and, if defined, in a given one of the instruction templates of thatinstruction format) and specifies or indicates the operation and theoperands upon which the operation will operate.

Scientific, financial, auto-vectorized general purpose, RMS(recognition, mining, and synthesis), and visual and multimediaapplications (e.g., 2D/3D graphics, image processing, videocompression/decompression, voice recognition algorithms and audiomanipulation) may require the same operation to be performed on a largenumber of data items. In one embodiment, Single Instruction MultipleData (SIMD) refers to a type of instruction that causes a processor toperform an operation on multiple data elements. SIMD technology may beused in processors that may logically divide the bits in a register intoa number of fixed-sized or variable-sized data elements, each of whichrepresents a separate value. For example, in one embodiment, the bits ina 64-bit register may be organized as a source operand containing fourseparate 16-bit data elements, each of which represents a separate16-bit value. This type of data may be referred to as ‘packed’ data typeor ‘vector’ data type, and operands of this data type may be referred toas packed data operands or vector operands. In one embodiment, a packeddata item or vector may be a sequence of packed data elements storedwithin a single register, and a packed data operand or a vector operandmay a source or destination operand of a SIMD instruction (or ‘packeddata instruction’ or a ‘vector instruction’). In one embodiment, a SIMDinstruction specifies a single vector operation to be performed on twosource vector operands to generate a destination vector operand (alsoreferred to as a result vector operand) of the same or different size,with the same or different number of data elements, and in the same ordifferent data element order.

SIMD technology, such as that employed by the Intel® Core™ processorshaving an instruction set including x86, MMX™, Streaming SIMD Extensions(SSE), SSE2, SSE3, SSE4.1, and SSE4.2 instructions, ARM processors, suchas the ARM Cortex® family of processors having an instruction setincluding the Vector Floating Point (VFP) and/or NEON instructions, andMIPS processors, such as the Loongson family of processors developed bythe Institute of Computing Technology (ICT) of the Chinese Academy ofSciences, has enabled a significant improvement in applicationperformance (Core™ and MMX™ are registered trademarks or trademarks ofIntel Corporation of Santa Clara, Calif.).

In one embodiment, destination and source registers/data may be genericterms to represent the source and destination of the corresponding dataor operation. In some embodiments, they may be implemented by registers,memory, or other storage areas having other names or functions thanthose depicted. For example, in one embodiment, “DEST1” may be atemporary storage register or other storage area, whereas “SRC1” and“SRC2” may be a first and second source storage register or otherstorage area, and so forth. In other embodiments, two or more of the SRCand DEST storage areas may correspond to different data storage elementswithin the same storage area (e.g., a SIMD register). In one embodiment,one of the source registers may also act as a destination register by,for example, writing back the result of an operation performed on thefirst and second source data to one of the two source registers servingas a destination registers.

FIG. 1A is a block diagram of an exemplary computer system formed with aprocessor that may include execution units to execute an instruction, inaccordance with embodiments of the present disclosure. System 100 mayinclude a component, such as a processor 102 to employ execution unitsincluding logic to perform algorithms for process data, in accordancewith the present disclosure, such as in the embodiment described herein.System 100 may be representative of processing systems based on thePENTIUM® III, PENTIUM® 4, Xeon™, Itanium®, XScale™ and/or StrongARM™microprocessors available from Intel Corporation of Santa Clara, Calif.,although other systems (including PCs having other microprocessors,engineering workstations, set-top boxes and the like) may also be used.In one embodiment, sample system 100 may execute a version of theWINDOWS™ operating system available from Microsoft Corporation ofRedmond, Wash., although other operating systems (UNIX and Linux forexample), embedded software, and/or graphical user interfaces, may alsobe used. Thus, embodiments of the present disclosure are not limited toany specific combination of hardware circuitry and software.

Embodiments are not limited to computer systems. Embodiments of thepresent disclosure may be used in other devices such as handheld devicesand embedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (PDAs), and handheld PCs. Embedded applications mayinclude a micro controller, a digital signal processor (DSP), system ona chip, network computers (NetPC), set-top boxes, network hubs, widearea network (WAN) switches, or any other system that may perform one ormore instructions in accordance with at least one embodiment.

Computer system 100 may include a processor 102 that may include one ormore execution units 108 to perform an algorithm to perform at least oneinstruction in accordance with one embodiment of the present disclosure.One embodiment may be described in the context of a single processordesktop or server system, but other embodiments may be included in amultiprocessor system. System 100 may be an example of a ‘hub’ systemarchitecture. System 100 may include a processor 102 for processing datasignals. Processor 102 may include a complex instruction set computer(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. Inone embodiment, processor 102 may be coupled to a processor bus 110 thatmay transmit data signals between processor 102 and other components insystem 100. The elements of system 100 may perform conventionalfunctions that are well known to those familiar with the art.

In one embodiment, processor 102 may include a Level 1 (L1) internalcache memory 104. Depending on the architecture, the processor 102 mayhave a single internal cache or multiple levels of internal cache. Inanother embodiment, the cache memory may reside external to processor102. Other embodiments may also include a combination of both internaland external caches depending on the particular implementation andneeds. Register file 106 may store different types of data in variousregisters including integer registers, floating point registers, statusregisters, and instruction pointer register.

Execution unit 108, including logic to perform integer and floatingpoint operations, also resides in processor 102. Processor 102 may alsoinclude a microcode (ucode) ROM that stores microcode for certainmacroinstructions. In one embodiment, execution unit 108 may includelogic to handle a packed instruction set 109. By including the packedinstruction set 109 in the instruction set of a general-purposeprocessor 102, along with associated circuitry to execute theinstructions, the operations used by many multimedia applications may beperformed using packed data in a general-purpose processor 102. Thus,many multimedia applications may be accelerated and executed moreefficiently by using the full width of a processor's data bus forperforming operations on packed data. This may eliminate the need totransfer smaller units of data across the processor's data bus toperform one or more operations one data element at a time.

Embodiments of an execution unit 108 may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. System 100 may include a memory 120. Memory 120may be implemented as a dynamic random access memory (DRAM) device, astatic random access memory (SRAM) device, flash memory device, or othermemory device. Memory 120 may store instructions and/or data representedby data signals that may be executed by processor 102.

A system logic chip 116 may be coupled to processor bus 110 and memory120. System logic chip 116 may include a memory controller hub (MCH).Processor 102 may communicate with MCH 116 via a processor bus 110. MCH116 may provide a high bandwidth memory path 118 to memory 120 forinstruction and data storage and for storage of graphics commands, dataand textures. MCH 116 may direct data signals between processor 102,memory 120, and other components in system 100 and to bridge the datasignals between processor bus 110, memory 120, and system I/O 122. Insome embodiments, the system logic chip 116 may provide a graphics portfor coupling to a graphics controller 112. MCH 116 may be coupled tomemory 120 through a memory interface 118. Graphics card 112 may becoupled to MCH 116 through an Accelerated Graphics Port (AGP)interconnect 114.

System 100 may use a proprietary hub interface bus 122 to couple MCH 116to I/O controller hub (ICH) 130. In one embodiment, ICH 130 may providedirect connections to some I/O devices via a local I/O bus. The localI/O bus may include a high-speed I/O bus for connecting peripherals tomemory 120, chipset, and processor 102. Examples may include the audiocontroller, firmware hub (flash BIOS) 128, wireless transceiver 126,data storage 124, legacy I/O controller containing user input andkeyboard interfaces, a serial expansion port such as Universal SerialBus (USB), and a network controller 134. Data storage device 124 maycomprise a hard disk drive, a floppy disk drive, a CD-ROM device, aflash memory device, or other mass storage device.

For another embodiment of a system, an instruction in accordance withone embodiment may be used with a system on a chip. One embodiment of asystem on a chip comprises of a processor and a memory. The memory forone such system may include a flash memory. The flash memory may belocated on the same die as the processor and other system components.Additionally, other logic blocks such as a memory controller or graphicscontroller may also be located on a system on a chip.

FIG. 1B illustrates a data processing system 140 which implements theprinciples of embodiments of the present disclosure. It will be readilyappreciated by one of skill in the art that the embodiments describedherein may operate with alternative processing systems without departurefrom the scope of embodiments of the disclosure.

Computer system 140 comprises a processing core 159 for performing atleast one instruction in accordance with one embodiment. In oneembodiment, processing core 159 represents a processing unit of any typeof architecture, including but not limited to a CISC, a RISC or a VLIWtype architecture. Processing core 159 may also be suitable formanufacture in one or more process technologies and by being representedon a machine-readable media in sufficient detail, may be suitable tofacilitate said manufacture.

Processing core 159 comprises an execution unit 142, a set of registerfiles 145, and a decoder 144. Processing core 159 may also includeadditional circuitry (not shown) which may be unnecessary to theunderstanding of embodiments of the present disclosure. Execution unit142 may execute instructions received by processing core 159. Inaddition to performing typical processor instructions, execution unit142 may perform instructions in packed instruction set 143 forperforming operations on packed data formats. Packed instruction set 143may include instructions for performing embodiments of the disclosureand other packed instructions. Execution unit 142 may be coupled toregister file 145 by an internal bus. Register file 145 may represent astorage area on processing core 159 for storing information, includingdata. As previously mentioned, it is understood that the storage areamay store the packed data might not be critical. Execution unit 142 maybe coupled to decoder 144. Decoder 144 may decode instructions receivedby processing core 159 into control signals and/or microcode entrypoints. In response to these control signals and/or microcode entrypoints, execution unit 142 performs the appropriate operations. In oneembodiment, the decoder may interpret the opcode of the instruction,which will indicate what operation should be performed on thecorresponding data indicated within the instruction.

Processing core 159 may be coupled with bus 141 for communicating withvarious other system devices, which may include but are not limited to,for example, synchronous dynamic random access memory (SDRAM) control146, static random access memory (SRAM) control 147, burst flash memoryinterface 148, personal computer memory card international association(PCMCIA)/compact flash (CF) card control 149, liquid crystal display(LCD) control 150, direct memory access (DMA) controller 151, andalternative bus master interface 152. In one embodiment, data processingsystem 140 may also comprise an I/O bridge 154 for communicating withvarious I/O devices via an I/O bus 153. Such I/O devices may include butare not limited to, for example, universal asynchronousreceiver/transmitter (UART) 155, universal serial bus (USB) 156,Bluetooth wireless UART 157 and I/O expansion interface 158.

One embodiment of data processing system 140 provides for mobile,network and/or wireless communications and a processing core 159 thatmay perform SIMD operations including a text string comparisonoperation. Processing core 159 may be programmed with various audio,video, imaging and communications algorithms including discretetransformations such as a Walsh-Hadamard transform, a fast Fouriertransform (FFT), a discrete cosine transform (DCT), and their respectiveinverse transforms; compression/decompression techniques such as colorspace transformation, video encode motion estimation or video decodemotion compensation; and modulation/demodulation (MODEM) functions suchas pulse coded modulation (PCM).

FIG. 1C illustrates other embodiments of a data processing system thatperforms SIMD text string comparison operations. In one embodiment, dataprocessing system 160 may include a main processor 166, a SIMDcoprocessor 161, a cache memory 167, and an input/output system 168.Input/output system 168 may optionally be coupled to a wirelessinterface 169. SIMD coprocessor 161 may perform operations includinginstructions in accordance with one embodiment. In one embodiment,processing core 170 may be suitable for manufacture in one or moreprocess technologies and by being represented on a machine-readablemedia in sufficient detail, may be suitable to facilitate themanufacture of all or part of data processing system 160 includingprocessing core 170.

In one embodiment, SIMD coprocessor 161 comprises an execution unit 162and a set of register files 164. One embodiment of main processor 165comprises a decoder 165 to recognize instructions of instruction set 163including instructions in accordance with one embodiment for executionby execution unit 162. In other embodiments, SIMD coprocessor 161 alsocomprises at least part of decoder 165 to decode instructions ofinstruction set 163. Processing core 170 may also include additionalcircuitry (not shown) which may be unnecessary to the understanding ofembodiments of the present disclosure.

In operation, main processor 166 executes a stream of data processinginstructions that control data processing operations of a general typeincluding interactions with cache memory 167, and input/output system168. Embedded within the stream of data processing instructions may beSIMD coprocessor instructions. Decoder 165 of main processor 166recognizes these SIMD coprocessor instructions as being of a type thatshould be executed by an attached SIMD coprocessor 161. Accordingly,main processor 166 issues these SIMD coprocessor instructions (orcontrol signals representing SIMD coprocessor instructions) on thecoprocessor bus 166. From coprocessor bus 166, these instructions may bereceived by any attached SIMD coprocessors. In this case, SIMDcoprocessor 161 may accept and execute any received SIMD coprocessorinstructions intended for it.

Data may be received via wireless interface 169 for processing by theSIMD coprocessor instructions. For one example, voice communication maybe received in the form of a digital signal, which may be processed bythe SIMD coprocessor instructions to regenerate digital audio samplesrepresentative of the voice communications. For another example,compressed audio and/or video may be received in the form of a digitalbit stream, which may be processed by the SIMD coprocessor instructionsto regenerate digital audio samples and/or motion video frames. In oneembodiment of processing core 170, main processor 166, and a SIMDcoprocessor 161 may be integrated into a single processing core 170comprising an execution unit 162, a set of register files 164, and adecoder 165 to recognize instructions of instruction set 163 includinginstructions in accordance with one embodiment.

FIG. 2 is a block diagram of the micro-architecture for a processor 200that may include logic circuits to perform instructions, in accordancewith embodiments of the present disclosure. In some embodiments, aninstruction in accordance with one embodiment may be implemented tooperate on data elements having sizes of byte, word, doubleword,quadword, etc., as well as datatypes, such as single and doubleprecision integer and floating point datatypes. In one embodiment,in-order front end 201 may implement a part of processor 200 that mayfetch instructions to be executed and prepares the instructions to beused later in the processor pipeline. Front end 201 may include severalunits. In one embodiment, instruction prefetcher 226 fetchesinstructions from memory and feeds the instructions to an instructiondecoder 228 which in turn decodes or interprets the instructions. Forexample, in one embodiment, the decoder decodes a received instructioninto one or more operations called “micro-instructions” or“micro-operations” (also called micro op or uops) that the machine mayexecute. In other embodiments, the decoder parses the instruction intoan opcode and corresponding data and control fields that may be used bythe micro-architecture to perform operations in accordance with oneembodiment. In one embodiment, trace cache 230 may assemble decoded uopsinto program ordered sequences or traces in uop queue 234 for execution.When trace cache 230 encounters a complex instruction, microcode ROM 232provides the uops needed to complete the operation.

Some instructions may be converted into a single micro-op, whereasothers need several micro-ops to complete the full operation. In oneembodiment, if more than four micro-ops are needed to complete aninstruction, decoder 228 may access microcode ROM 232 to perform theinstruction. In one embodiment, an instruction may be decoded into asmall number of micro ops for processing at instruction decoder 228. Inanother embodiment, an instruction may be stored within microcode ROM232 should a number of micro-ops be needed to accomplish the operation.Trace cache 230 refers to an entry point programmable logic array (PLA)to determine a correct micro-instruction pointer for reading themicro-code sequences to complete one or more instructions in accordancewith one embodiment from micro-code ROM 232. After microcode ROM 232finishes sequencing micro-ops for an instruction, front end 201 of themachine may resume fetching micro-ops from trace cache 230.

Out-of-order execution engine 203 may prepare instructions forexecution. The out-of-order execution logic has a number of buffers tosmooth out and re-order the flow of instructions to optimize performanceas they go down the pipeline and get scheduled for execution. Theallocator logic allocates the machine buffers and resources that eachuop needs in order to execute. The register renaming logic renames logicregisters onto entries in a register file. The allocator also allocatesan entry for each uop in one of the two uop queues, one for memoryoperations and one for non-memory operations, in front of theinstruction schedulers: memory scheduler, fast scheduler 202,slow/general floating point scheduler 204, and simple floating pointscheduler 206. Uop schedulers 202, 204, 206, determine when a uop isready to execute based on the readiness of their dependent inputregister operand sources and the availability of the execution resourcesthe uops need to complete their operation. Fast scheduler 202 of oneembodiment may schedule on each half of the main clock cycle while theother schedulers may only schedule once per main processor clock cycle.The schedulers arbitrate for the dispatch ports to schedule uops forexecution.

Register files 208, 210 may be arranged between schedulers 202, 204,206, and execution units 212, 214, 216, 218, 220, 222, 224 in executionblock 211. Each of register files 208, 210 perform integer and floatingpoint operations, respectively. Each register file 208, 210, may includea bypass network that may bypass or forward just completed results thathave not yet been written into the register file to new dependent uops.Integer register file 208 and floating point register file 210 maycommunicate data with the other. In one embodiment, integer registerfile 208 may be split into two separate register files, one registerfile for low-order thirty-two bits of data and a second register filefor high order thirty-two bits of data. Floating point register file 210may include 128-bit wide entries because floating point instructionstypically have operands from 64 to 128 bits in width.

Execution block 211 may contain execution units 212, 214, 216, 218, 220,222, 224. Execution units 212, 214, 216, 218, 220, 222, 224 may executethe instructions. Execution block 211 may include register files 208,210 that store the integer and floating point data operand values thatthe micro-instructions need to execute. In one embodiment, processor 200may comprise a number of execution units: address generation unit (AGU)212, AGU 214, fast ALU 216, fast ALU 218, slow ALU 220, floating pointALU 222, floating point move unit 224. In another embodiment, floatingpoint execution blocks 222, 224, may execute floating point, MMX, SIMD,and SSE, or other operations. In yet another embodiment, floating pointALU 222 may include a 64-bit by 64-bit floating point divider to executedivide, square root, and remainder micro-ops. In various embodiments,instructions involving a floating point value may be handled with thefloating point hardware. In one embodiment, ALU operations may be passedto high-speed ALU execution units 216, 218. High-speed ALUs 216, 218 mayexecute fast operations with an effective latency of half a clock cycle.In one embodiment, most complex integer operations go to slow ALU 220 asslow ALU 220 may include integer execution hardware for long-latencytype of operations, such as a multiplier, shifts, flag logic, and branchprocessing. Memory load/store operations may be executed by AGUs 212,214. In one embodiment, integer ALUs 216, 218, 220 may perform integeroperations on 64-bit data operands. In other embodiments, ALUs 216, 218,220 may be implemented to support a variety of data bit sizes includingsixteen, thirty-two, 128, 256, etc. Similarly, floating point units 222,224 may be implemented to support a range of operands having bits ofvarious widths. In one embodiment, floating point units 222, 224, mayoperate on 128-bit wide packed data operands in conjunction with SIMDand multimedia instructions.

In one embodiment, uops schedulers 202, 204, 206, dispatch dependentoperations before the parent load has finished executing. As uops may bespeculatively scheduled and executed in processor 200, processor 200 mayalso include logic to handle memory misses. If a data load misses in thedata cache, there may be dependent operations in flight in the pipelinethat have left the scheduler with temporarily incorrect data. A replaymechanism tracks and re-executes instructions that use incorrect data.Only the dependent operations might need to be replayed and theindependent ones may be allowed to complete. The schedulers and replaymechanism of one embodiment of a processor may also be designed to catchinstruction sequences for text string comparison operations.

The term “registers” may refer to the on-board processor storagelocations that may be used as part of instructions to identify operands.In other words, registers may be those that may be usable from theoutside of the processor (from a programmer's perspective). However, insome embodiments registers might not be limited to a particular type ofcircuit. Rather, a register may store data, provide data, and performthe functions described herein. The registers described herein may beimplemented by circuitry within a processor using any number ofdifferent techniques, such as dedicated physical registers, dynamicallyallocated physical registers using register renaming, combinations ofdedicated and dynamically allocated physical registers, etc. In oneembodiment, integer registers store 32-bit integer data. A register fileof one embodiment also contains eight multimedia SIMD registers forpacked data. For the discussions below, the registers may be understoodto be data registers designed to hold packed data, such as 64-bit wideMMX™ registers (also referred to as ‘mm’ registers in some instances) inmicroprocessors enabled with MMX technology from Intel Corporation ofSanta Clara, Calif. These MMX registers, available in both integer andfloating point forms, may operate with packed data elements thataccompany SIMD and SSE instructions. Similarly, 128-bit wide XMMregisters relating to SSE2, SSE3, SSE4, or beyond (referred togenerically as “SSEx”) technology may hold such packed data operands. Inone embodiment, in storing packed data and integer data, the registersdo not need to differentiate between the two data types. In oneembodiment, integer and floating point may be contained in the sameregister file or different register files. Furthermore, in oneembodiment, floating point and integer data may be stored in differentregisters or the same registers.

In the examples of the following figures, a number of data operands maybe described. FIG. 3A illustrates various packed data typerepresentations in multimedia registers, in accordance with embodimentsof the present disclosure. FIG. 3A illustrates data types for a packedbyte 310, a packed word 320, and a packed doubleword (dword) 330 for128-bit wide operands. Packed byte format 310 of this example may be 128bits long and contains sixteen packed byte data elements. A byte may bedefined, for example, as eight bits of data. Information for each bytedata element may be stored in bit 7 through bit 0 for byte 0, bit 15through bit 8 for byte 1, bit 23 through bit 16 for byte 2, and finallybit 120 through bit 127 for byte 15. Thus, all available bits may beused in the register. This storage arrangement increases the storageefficiency of the processor. As well, with sixteen data elementsaccessed, one operation may now be performed on sixteen data elements inparallel.

Generally, a data element may include an individual piece of data thatis stored in a single register or memory location with other dataelements of the same length. In packed data sequences relating to SSExtechnology, the number of data elements stored in a XMM register may be128 bits divided by the length in bits of an individual data element.Similarly, in packed data sequences relating to MMX and SSE technology,the number of data elements stored in an MMX register may be 64 bitsdivided by the length in bits of an individual data element. Althoughthe data types illustrated in FIG. 3A may be 128 bits long, embodimentsof the present disclosure may also operate with 64-bit wide or othersized operands. Packed word format 320 of this example may be 128 bitslong and contains eight packed word data elements. Each packed wordcontains sixteen bits of information. Packed doubleword format 330 ofFIG. 3A may be 128 bits long and contains four packed doubleword dataelements. Each packed doubleword data element contains thirty-two bitsof information. A packed quadword may be 128 bits long and contain twopacked quad-word data elements.

FIG. 3B illustrates possible in-register data storage formats, inaccordance with embodiments of the present disclosure. Each packed datamay include more than one independent data element. Three packed dataformats are illustrated; packed half 341, packed single 342, and packeddouble 343. One embodiment of packed half 341, packed single 342, andpacked double 343 contain fixed-point data elements. For anotherembodiment one or more of packed half 341, packed single 342, and packeddouble 343 may contain floating-point data elements. One embodiment ofpacked half 341 may be 128 bits long containing eight 16-bit dataelements. One embodiment of packed single 342 may be 128 bits long andcontains four 32-bit data elements. One embodiment of packed double 343may be 128 bits long and contains two 64-bit data elements. It will beappreciated that such packed data formats may be further extended toother register lengths, for example, to 96-bits, 160-bits, 192-bits,224-bits, 256-bits or more.

FIG. 3C illustrates various signed and unsigned packed data typerepresentations in multimedia registers, in accordance with embodimentsof the present disclosure. Unsigned packed byte representation 344illustrates the storage of an unsigned packed byte in a SIMD register.Information for each byte data element may be stored in bit 7 throughbit 0 for byte 0, bit 15 through bit 8 for byte 1, bit 23 through bit 16for byte 2, and finally bit 120 through bit 127 for byte 15. Thus, allavailable bits may be used in the register. This storage arrangement mayincrease the storage efficiency of the processor. As well, with sixteendata elements accessed, one operation may now be performed on sixteendata elements in a parallel fashion. Signed packed byte representation345 illustrates the storage of a signed packed byte. Note that theeighth bit of every byte data element may be the sign indicator.Unsigned packed word representation 346 illustrates how word seventhrough word zero may be stored in a SIMD register. Signed packed wordrepresentation 347 may be similar to the unsigned packed wordin-register representation 346. Note that the sixteenth bit of each worddata element may be the sign indicator. Unsigned packed doublewordrepresentation 348 shows how doubleword data elements are stored. Signedpacked doubleword representation 349 may be similar to unsigned packeddoubleword in-register representation 348. Note that the necessary signbit may be the thirty-second bit of each doubleword data element.

FIG. 3D illustrates an embodiment of an operation encoding (opcode).Furthermore, format 360 may include register/memory operand addressingmodes corresponding with a type of opcode format described in the“IA-32. Intel Architecture Software Developer's Manual Volume 2:Instruction Set Reference,” which is available from Intel Corporation,Santa Clara, Calif. on the world-wide-web (www) atintel.com/design/litcentr. In one embodiment, and instruction may beencoded by one or more of fields 361 and 362. Up to two operandlocations per instruction may be identified, including up to two sourceoperand identifiers 364 and 365. In one embodiment, destination operandidentifier 366 may be the same as source operand identifier 364, whereasin other embodiments they may be different. In another embodiment,destination operand identifier 366 may be the same as source operandidentifier 365, whereas in other embodiments they may be different. Inone embodiment, one of the source operands identified by source operandidentifiers 364 and 365 may be overwritten by the results of the textstring comparison operations, whereas in other embodiments identifier364 corresponds to a source register element and identifier 365corresponds to a destination register element. In one embodiment,operand identifiers 364 and 365 may identify 32-bit or 64-bit source anddestination operands.

FIG. 3E illustrates another possible operation encoding (opcode) format370, having forty or more bits, in accordance with embodiments of thepresent disclosure. Opcode format 370 corresponds with opcode format 360and comprises an optional prefix byte 378. An instruction according toone embodiment may be encoded by one or more of fields 378, 371, and372. Up to two operand locations per instruction may be identified bysource operand identifiers 374 and 375 and by prefix byte 378. In oneembodiment, prefix byte 378 may be used to identify 32-bit or 64-bitsource and destination operands. In one embodiment, destination operandidentifier 376 may be the same as source operand identifier 374, whereasin other embodiments they may be different. For another embodiment,destination operand identifier 376 may be the same as source operandidentifier 375, whereas in other embodiments they may be different. Inone embodiment, an instruction operates on one or more of the operandsidentified by operand identifiers 374 and 375 and one or more operandsidentified by operand identifiers 374 and 375 may be overwritten by theresults of the instruction, whereas in other embodiments, operandsidentified by identifiers 374 and 375 may be written to another dataelement in another register. Opcode formats 360 and 370 allow registerto register, memory to register, register by memory, register byregister, register by immediate, register to memory addressing specifiedin part by MOD fields 363 and 373 and by optional scale-index-base anddisplacement bytes.

FIG. 3F illustrates yet another possible operation encoding (opcode)format, in accordance with embodiments of the present disclosure. 64-bitsingle instruction multiple data (SIMD) arithmetic operations may beperformed through a coprocessor data processing (CDP) instruction.Operation encoding (opcode) format 380 depicts one such CDP instructionhaving CDP opcode fields 382 an0064 389. The type of CDP instruction,for another embodiment, operations may be encoded by one or more offields 383, 384, 387, and 388. Up to three operand locations perinstruction may be identified, including up to two source operandidentifiers 385 and 390 and one destination operand identifier 386. Oneembodiment of the coprocessor may operate on eight, sixteen, thirty-two,and 64-bit values. In one embodiment, an instruction may be performed oninteger data elements. In some embodiments, an instruction may beexecuted conditionally, using condition field 381. For some embodiments,source data sizes may be encoded by field 383. In some embodiments, Zero(Z), negative (N), carry (C), and overflow (V) detection may be done onSIMD fields. For some instructions, the type of saturation may beencoded by field 384.

FIG. 4A is a block diagram illustrating an in-order pipeline and aregister renaming stage, out-of-order issue/execution pipeline, inaccordance with embodiments of the present disclosure. FIG. 4B is ablock diagram illustrating an in-order architecture core and a registerrenaming logic, out-of-order issue/execution logic to be included in aprocessor, in accordance with embodiments of the present disclosure. Thesolid lined boxes in FIG. 4A illustrate the in-order pipeline, while thedashed lined boxes illustrates the register renaming, out-of-orderissue/execution pipeline. Similarly, the solid lined boxes in FIG. 4Billustrate the in-order architecture logic, while the dashed lined boxesillustrates the register renaming logic and out-of-order issue/executionlogic.

In FIG. 4A, a processor pipeline 400 may include a fetch stage 402, alength decode stage 404, a decode stage 406, an allocation stage 408, arenaming stage 410, a scheduling (also known as a dispatch or issue)stage 412, a register read/memory read stage 414, an execute stage 416,a write-back/memory-write stage 418, an exception handling stage 422,and a commit stage 424.

In FIG. 4B, arrows denote a coupling between two or more units and thedirection of the arrow indicates a direction of data flow between thoseunits. FIG. 4B shows processor core 490 including a front end unit 430coupled to an execution engine unit 450, and both may be coupled to amemory unit 470.

Core 490 may be a reduced instruction set computing (RISC) core, acomplex instruction set computing (CISC) core, a very long instructionword (VLIW) core, or a hybrid or alternative core type. In oneembodiment, core 490 may be a special-purpose core, such as, forexample, a network or communication core, compression engine, graphicscore, or the like.

Front end unit 430 may include a branch prediction unit 432 coupled toan instruction cache unit 434. Instruction cache unit 434 may be coupledto an instruction translation lookaside buffer (TLB) 436. TLB 436 may becoupled to an instruction fetch unit 438, which is coupled to a decodeunit 440. Decode unit 440 may decode instructions, and generate as anoutput one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichmay be decoded from, or which otherwise reflect, or may be derived from,the original instructions. The decoder may be implemented using variousdifferent mechanisms. Examples of suitable mechanisms include, but arenot limited to, look-up tables, hardware implementations, programmablelogic arrays (PLAs), microcode read-only memories (ROMs), etc. In oneembodiment, instruction cache unit 434 may be further coupled to a level2 (L2) cache unit 476 in memory unit 470. Decode unit 440 may be coupledto a rename/allocator unit 452 in execution engine unit 450.

Execution engine unit 450 may include rename/allocator unit 452 coupledto a retirement unit 454 and a set of one or more scheduler units 456.Scheduler units 456 represent any number of different schedulers,including reservations stations, central instruction window, etc.Scheduler units 456 may be coupled to physical register file units 458.Each of physical register file units 458 represents one or more physicalregister files, different ones of which store one or more different datatypes, such as scalar integer, scalar floating point, packed integer,packed floating point, vector integer, vector floating point, etc.,status (e.g., an instruction pointer that is the address of the nextinstruction to be executed), etc. Physical register file units 458 maybe overlapped by retirement unit 154 to illustrate various ways in whichregister renaming and out-of-order execution may be implemented (e.g.,using one or more reorder buffers and one or more retirement registerfiles, using one or more future files, one or more history buffers, andone or more retirement register files; using register maps and a pool ofregisters; etc.). Generally, the architectural registers may be visiblefrom the outside of the processor or from a programmer's perspective.The registers might not be limited to any known particular type ofcircuit. Various different types of registers may be suitable as long asthey store and provide data as described herein. Examples of suitableregisters include, but might not be limited to, dedicated physicalregisters, dynamically allocated physical registers using registerrenaming, combinations of dedicated and dynamically allocated physicalregisters, etc. Retirement unit 454 and physical register file units 458may be coupled to execution clusters 460. Execution clusters 460 mayinclude a set of one or more execution units 162 and a set of one ormore memory access units 464. Execution units 462 may perform variousoperations (e.g., shifts, addition, subtraction, multiplication) and onvarious types of data (e.g., scalar floating point, packed integer,packed floating point, vector integer, vector floating point). Whilesome embodiments may include a number of execution units dedicated tospecific functions or sets of functions, other embodiments may includeonly one execution unit or multiple execution units that all perform allfunctions. Scheduler units 456, physical register file units 458, andexecution clusters 460 are shown as being possibly plural becausecertain embodiments create separate pipelines for certain types ofdata/operations (e.g., a scalar integer pipeline, a scalar floatingpoint/packed integer/packed floating point/vector integer/vectorfloating point pipeline, and/or a memory access pipeline that each havetheir own scheduler unit, physical register file unit, and/or executioncluster—and in the case of a separate memory access pipeline, certainembodiments may be implemented in which only the execution cluster ofthis pipeline has memory access units 464). It should also be understoodthat where separate pipelines are used, one or more of these pipelinesmay be out-of-order issue/execution and the rest in-order.

The set of memory access units 464 may be coupled to memory unit 470,which may include a data TLB unit 472 coupled to a data cache unit 474coupled to a level 2 (L2) cache unit 476. In one exemplary embodiment,memory access units 464 may include a load unit, a store address unit,and a store data unit, each of which may be coupled to data TLB unit 472in memory unit 470. L2 cache unit 476 may be coupled to one or moreother levels of cache and eventually to a main memory.

By way of example, the exemplary register renaming, out-of-orderissue/execution core architecture may implement pipeline 400 asfollows: 1) instruction fetch 438 may perform fetch and length decodingstages 402 and 404; 2) decode unit 440 may perform decode stage 406; 3)rename/allocator unit 452 may perform allocation stage 408 and renamingstage 410; 4) scheduler units 456 may perform schedule stage 412; 5)physical register file units 458 and memory unit 470 may performregister read/memory read stage 414; execution cluster 460 may performexecute stage 416; 6) memory unit 470 and physical register file units458 may perform write-back/memory-write stage 418; 7) various units maybe involved in the performance of exception handling stage 422; and 8)retirement unit 454 and physical register file units 458 may performcommit stage 424.

Core 490 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.).

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads) in avariety of manners. Multithreading support may be performed by, forexample, including time sliced multithreading, simultaneousmultithreading (where a single physical core provides a logical core foreach of the threads that physical core is simultaneouslymultithreading), or a combination thereof. Such a combination mayinclude, for example, time sliced fetching and decoding and simultaneousmultithreading thereafter such as in the Intel® Hyperthreadingtechnology.

While register renaming may be described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor may also include a separate instruction and data cache units434/474 and a shared L2 cache unit 476, other embodiments may have asingle internal cache for both instructions and data, such as, forexample, a Level 1 (L1) internal cache, or multiple levels of internalcache. In some embodiments, the system may include a combination of aninternal cache and an external cache that may be external to the coreand/or the processor. In other embodiments, all of the cache may beexternal to the core and/or the processor.

FIG. 5A is a block diagram of a processor 500, in accordance withembodiments of the present disclosure. In one embodiment, processor 500may include a multicore processor. Processor 500 may include a systemagent 510 communicatively coupled to one or more cores 502. Furthermore,cores 502 and system agent 510 may be communicatively coupled to one ormore caches 506. Cores 502, system agent 510, and caches 506 may becommunicatively coupled via one or more memory control units 552.Furthermore, cores 502, system agent 510, and caches 506 may becommunicatively coupled to a graphics module 560 via memory controlunits 552.

Processor 500 may include any suitable mechanism for interconnectingcores 502, system agent 510, and caches 506, and graphics module 560. Inone embodiment, processor 500 may include a ring-based interconnect unit508 to interconnect cores 502, system agent 510, and caches 506, andgraphics module 560. In other embodiments, processor 500 may include anynumber of well-known techniques for interconnecting such units.Ring-based interconnect unit 508 may utilize memory control units 552 tofacilitate interconnections.

Processor 500 may include a memory hierarchy comprising one or morelevels of caches within the cores, one or more shared cache units suchas caches 506, or external memory (not shown) coupled to the set ofintegrated memory controller units 552. Caches 506 may include anysuitable cache. In one embodiment, caches 506 may include one or moremid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), orother levels of cache, a last level cache (LLC), and/or combinationsthereof.

In various embodiments, one or more of cores 502 may performmultithreading. System agent 510 may include components for coordinatingand operating cores 502. System agent unit 510 may include for example apower control unit (PCU). The PCU may be or include logic and componentsneeded for regulating the power state of cores 502. System agent 510 mayinclude a display engine 512 for driving one or more externallyconnected displays or graphics module 560. System agent 510 may includean interface 1214 for communications busses for graphics. In oneembodiment, interface 1214 may be implemented by PCI Express (PCIe). Ina further embodiment, interface 1214 may be implemented by PCI ExpressGraphics (PEG). System agent 510 may include a direct media interface(DMI) 516. DMI 516 may provide links between different bridges on amotherboard or other portion of a computer system. System agent 510 mayinclude a PCIe bridge 1218 for providing PCIe links to other elements ofa computing system. PCIe bridge 1218 may be implemented using a memorycontroller 1220 and coherence logic 1222.

Cores 502 may be implemented in any suitable manner. Cores 502 may behomogenous or heterogeneous in terms of architecture and/or instructionset. In one embodiment, some of cores 502 may be in-order while othersmay be out-of-order. In another embodiment, two or more of cores 502 mayexecute the same instruction set, while others may execute only a subsetof that instruction set or a different instruction set.

Processor 500 may include a general-purpose processor, such as a Core™i3, i5, i7, 2 Duo and Quad, Xeon™, Itanium™, XScale™ or StrongARM™processor, which may be available from Intel Corporation, of SantaClara, Calif. Processor 500 may be provided from another company, suchas ARM Holdings, Ltd, MIPS, etc. Processor 500 may be a special-purposeprocessor, such as, for example, a network or communication processor,compression engine, graphics processor, co-processor, embeddedprocessor, or the like. Processor 500 may be implemented on one or morechips. Processor 500 may be a part of and/or may be implemented on oneor more substrates using any of a number of process technologies, suchas, for example, BiCMOS, CMOS, or NMOS.

In one embodiment, a given one of caches 506 may be shared by multipleones of cores 502. In another embodiment, a given one of caches 506 maybe dedicated to one of cores 502. The assignment of caches 506 to cores502 may be handled by a cache controller or other suitable mechanism. Agiven one of caches 506 may be shared by two or more cores 502 byimplementing time-slices of a given cache 506.

Graphics module 560 may implement an integrated graphics processingsubsystem. In one embodiment, graphics module 560 may include a graphicsprocessor. Furthermore, graphics module 560 may include a media engine565. Media engine 565 may provide media encoding and video decoding.

FIG. 5B is a block diagram of an example implementation of a core 502,in accordance with embodiments of the present disclosure. Core 502 mayinclude a front end 570 communicatively coupled to an out-of-orderengine 580. Core 502 may be communicatively coupled to other portions ofprocessor 500 through cache hierarchy 503.

Front end 570 may be implemented in any suitable manner, such as fullyor in part by front end 201 as described above. In one embodiment, frontend 570 may communicate with other portions of processor 500 throughcache hierarchy 503. In a further embodiment, front end 570 may fetchinstructions from portions of processor 500 and prepare the instructionsto be used later in the processor pipeline as they are passed toout-of-order execution engine 580.

Out-of-order execution engine 580 may be implemented in any suitablemanner, such as fully or in part by out-of-order execution engine 203 asdescribed above. Out-of-order execution engine 580 may prepareinstructions received from front end 570 for execution. Out-of-orderexecution engine 580 may include an allocate module 1282. In oneembodiment, allocate module 1282 may allocate resources of processor 500or other resources, such as registers or buffers, to execute a giveninstruction. Allocate module 1282 may make allocations in schedulers,such as a memory scheduler, fast scheduler, or floating point scheduler.Such schedulers may be represented in FIG. 5B by resource schedulers584. Allocate module 1282 may be implemented fully or in part by theallocation logic described in conjunction with FIG. 2. Resourceschedulers 584 may determine when an instruction is ready to executebased on the readiness of a given resource's sources and theavailability of execution resources needed to execute an instruction.Resource schedulers 584 may be implemented by, for example, schedulers202, 204, 206 as discussed above. Resource schedulers 584 may schedulethe execution of instructions upon one or more resources. In oneembodiment, such resources may be internal to core 502, and may beillustrated, for example, as resources 586. In another embodiment, suchresources may be external to core 502 and may be accessible by, forexample, cache hierarchy 503. Resources may include, for example,memory, caches, register files, or registers. Resources internal to core502 may be represented by resources 586 in FIG. 5B. As necessary, valueswritten to or read from resources 586 may be coordinated with otherportions of processor 500 through, for example, cache hierarchy 503. Asinstructions are assigned resources, they may be placed into a reorderbuffer 588. Reorder buffer 588 may track instructions as they areexecuted and may selectively reorder their execution based upon anysuitable criteria of processor 500. In one embodiment, reorder buffer588 may identify instructions or a series of instructions that may beexecuted independently. Such instructions or a series of instructionsmay be executed in parallel from other such instructions. Parallelexecution in core 502 may be performed by any suitable number ofseparate execution blocks or virtual processors. In one embodiment,shared resources—such as memory, registers, and caches—may be accessibleto multiple virtual processors within a given core 502. In otherembodiments, shared resources may be accessible to multiple processingentities within processor 500.

Cache hierarchy 503 may be implemented in any suitable manner. Forexample, cache hierarchy 503 may include one or more lower or mid-levelcaches, such as caches 572, 574. In one embodiment, cache hierarchy 503may include an LLC 595 communicatively coupled to caches 572, 574. Inanother embodiment, LLC 595 may be implemented in a module 590accessible to all processing entities of processor 500. In a furtherembodiment, module 590 may be implemented in an uncore module ofprocessors from Intel, Inc. Module 590 may include portions orsubsystems of processor 500 necessary for the execution of core 502 butmight not be implemented within core 502. Besides LLC 595, Module 590may include, for example, hardware interfaces, memory coherencycoordinators, interprocessor interconnects, instruction pipelines, ormemory controllers. Access to RAM 599 available to processor 500 may bemade through module 590 and, more specifically, LLC 595. Furthermore,other instances of core 502 may similarly access module 590.Coordination of the instances of core 502 may be facilitated in partthrough module 590.

FIGS. 6-8 may illustrate exemplary systems suitable for includingprocessor 500, while FIG. 9 may illustrate an exemplary system on a chip(SoC) that may include one or more of cores 502. Other system designsand implementations known in the arts for laptops, desktops, handheldPCs, personal digital assistants, engineering workstations, servers,network devices, network hubs, switches, embedded processors, digitalsignal processors (DSPs), graphics devices, video game devices, set-topboxes, micro controllers, cell phones, portable media players, hand helddevices, and various other electronic devices, may also be suitable. Ingeneral, a huge variety of systems or electronic devices thatincorporate a processor and/or other execution logic as disclosed hereinmay be generally suitable.

FIG. 6 illustrates a block diagram of a system 600, in accordance withembodiments of the present disclosure. System 600 may include one ormore processors 610, 615, which may be coupled to graphics memorycontroller hub (GMCH) 620. The optional nature of additional processors615 is denoted in FIG. 6 with broken lines.

Each processor 610,615 may be some version of processor 500. However, itshould be noted that integrated graphics logic and integrated memorycontrol units might not exist in processors 610,615. FIG. 6 illustratesthat GMCH 620 may be coupled to a memory 640 that may be, for example, adynamic random access memory (DRAM). The DRAM may, for at least oneembodiment, be associated with a non-volatile cache.

GMCH 620 may be a chipset, or a portion of a chipset. GMCH 620 maycommunicate with processors 610, 615 and control interaction betweenprocessors 610, 615 and memory 640. GMCH 620 may also act as anaccelerated bus interface between the processors 610, 615 and otherelements of system 600. In one embodiment, GMCH 620 communicates withprocessors 610, 615 via a multi-drop bus, such as a frontside bus (FSB)695.

Furthermore, GMCH 620 may be coupled to a display 645 (such as a flatpanel display). In one embodiment, GMCH 620 may include an integratedgraphics accelerator. GMCH 620 may be further coupled to an input/output(I/O) controller hub (ICH) 650, which may be used to couple variousperipheral devices to system 600. External graphics device 660 mayinclude be a discrete graphics device coupled to ICH 650 along withanother peripheral device 670.

In other embodiments, additional or different processors may also bepresent in system 600. For example, additional processors 610, 615 mayinclude additional processors that may be the same as processor 610,additional processors that may be heterogeneous or asymmetric toprocessor 610, accelerators (such as, e.g., graphics accelerators ordigital signal processing (DSP) units), field programmable gate arrays,or any other processor. There may be a variety of differences betweenthe physical resources 610, 615 in terms of a spectrum of metrics ofmerit including architectural, micro-architectural, thermal, powerconsumption characteristics, and the like. These differences mayeffectively manifest themselves as asymmetry and heterogeneity amongstprocessors 610, 615. For at least one embodiment, various processors610, 615 may reside in the same die package.

FIG. 7 illustrates a block diagram of a second system 700, in accordancewith embodiments of the present disclosure. As shown in FIG. 7,multiprocessor system 700 may include a point-to-point interconnectsystem, and may include a first processor 770 and a second processor 780coupled via a point-to-point interconnect 750. Each of processors 770and 780 may be some version of processor 500 as one or more ofprocessors 610,615.

While FIG. 7 may illustrate two processors 770, 780, it is to beunderstood that the scope of the present disclosure is not so limited.In other embodiments, one or more additional processors may be presentin a given processor.

Processors 770 and 780 are shown including integrated memory controllerunits 772 and 782, respectively. Processor 770 may also include as partof its bus controller units point-to-point (P-P) interfaces 776 and 778;similarly, second processor 780 may include P-P interfaces 786 and 788.Processors 770, 780 may exchange information via a point-to-point (P-P)interface 750 using P-P interface circuits 778, 788. As shown in FIG. 7,IMCs 772 and 782 may couple the processors to respective memories,namely a memory 732 and a memory 734, which in one embodiment may beportions of main memory locally attached to the respective processors.

Processors 770, 780 may each exchange information with a chipset 790 viaindividual P-P interfaces 752, 754 using point to point interfacecircuits 776, 794, 786, 798. In one embodiment, chipset 790 may alsoexchange information with a high-performance graphics circuit 738 via ahigh-performance graphics interface 739.

A shared cache (not shown) may be included in either processor oroutside of both processors, yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 790 may be coupled to a first bus 716 via an interface 796. Inone embodiment, first bus 716 may be a Peripheral Component Interconnect(PCI) bus, or a bus such as a PCI Express bus or another thirdgeneration I/O interconnect bus, although the scope of the presentdisclosure is not so limited.

As shown in FIG. 7, various I/O devices 714 may be coupled to first bus716, along with a bus bridge 718 which couples first bus 716 to a secondbus 720. In one embodiment, second bus 720 may be a low pin count (LPC)bus. Various devices may be coupled to second bus 720 including, forexample, a keyboard and/or mouse 722, communication devices 727 and astorage unit 728 such as a disk drive or other mass storage device whichmay include instructions/code and data 730, in one embodiment. Further,an audio I/O 724 may be coupled to second bus 720. Note that otherarchitectures may be possible. For example, instead of thepoint-to-point architecture of FIG. 7, a system may implement amulti-drop bus or other such architecture.

FIG. 8 illustrates a block diagram of a third system 800 in accordancewith embodiments of the present disclosure. Like elements in FIGS. 7 and8 bear like reference numerals, and certain aspects of FIG. 7 have beenomitted from FIG. 8 in order to avoid obscuring other aspects of FIG. 8.

FIG. 8 illustrates that processors 870, 880 may include integratedmemory and I/O control logic (“CL”) 872 and 882, respectively. For atleast one embodiment, CL 872, 882 may include integrated memorycontroller units such as that described above in connection with FIGS. 5and 7. In addition. CL 872, 882 may also include I/O control logic. FIG.8 illustrates that not only memories 832, 834 may be coupled to CL 872,882, but also that I/O devices 814 may also be coupled to control logic872, 882. Legacy I/O devices 815 may be coupled to chipset 890.

FIG. 9 illustrates a block diagram of a SoC 900, in accordance withembodiments of the present disclosure. Similar elements in FIG. 5 bearlike reference numerals. Also, dashed lined boxes may represent optionalfeatures on more advanced SoCs. An interconnect units 902 may be coupledto: an application processor 910 which may include a set of one or morecores 902A-N and shared cache units 906; a system agent unit 910; a buscontroller units 916; an integrated memory controller units 914; a setor one or more media processors 920 which may include integratedgraphics logic 908, an image processor 924 for providing still and/orvideo camera functionality, an audio processor 926 for providinghardware audio acceleration, and a video processor 928 for providingvideo encode/decode acceleration; an static random access memory (SRAM)unit 930; a direct memory access (DMA) unit 932; and a display unit 940for coupling to one or more external displays.

FIG. 10 illustrates a processor containing a central processing unit(CPU) and a graphics processing unit (GPU), which may perform at leastone instruction, in accordance with embodiments of the presentdisclosure. In one embodiment, an instruction to perform operationsaccording to at least one embodiment could be performed by the CPU. Inanother embodiment, the instruction could be performed by the GPU. Instill another embodiment, the instruction may be performed through acombination of operations performed by the GPU and the CPU. For example,in one embodiment, an instruction in accordance with one embodiment maybe received and decoded for execution on the GPU. However, one or moreoperations within the decoded instruction may be performed by a CPU andthe result returned to the GPU for final retirement of the instruction.Conversely, in some embodiments, the CPU may act as the primaryprocessor and the GPU as the co-processor.

In some embodiments, instructions that benefit from highly parallel,throughput processors may be performed by the GPU, while instructionsthat benefit from the performance of processors that benefit from deeplypipelined architectures may be performed by the CPU. For example,graphics, scientific applications, financial applications and otherparallel workloads may benefit from the performance of the GPU and beexecuted accordingly, whereas more sequential applications, such asoperating system kernel or application code may be better suited for theCPU.

In FIG. 10, processor 1000 includes a CPU 1005, GPU 1010, imageprocessor 1015, video processor 1020, USB controller 1025, UARTcontroller 1030, SPI/SDIO controller 1035, display device 1040, memoryinterface controller 1045, MIPI controller 1050, flash memory controller1055, dual data rate (DDR) controller 1060, security engine 1065, andI²S/I²C controller 1070. Other logic and circuits may be included in theprocessor of FIG. 10, including more CPUs or GPUs and other peripheralinterface controllers.

One or more aspects of at least one embodiment may be implemented byrepresentative data stored on a machine-readable medium which representsvarious logic within the processor, which when read by a machine causesthe machine to fabricate logic to perform the techniques describedherein. Such representations, known as “IP cores” may be stored on atangible, machine-readable medium (“tape”) and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor. For example, IPcores, such as the Cortex™ family of processors developed by ARMHoldings, Ltd. and Loongson IP cores developed the Institute ofComputing Technology (ICT) of the Chinese Academy of Sciences may belicensed or sold to various customers or licensees, such as TexasInstruments, Qualcomm, Apple, or Samsung and implemented in processorsproduced by these customers or licensees.

FIG. 11 illustrates a block diagram illustrating the development of IPcores, in accordance with embodiments of the present disclosure. Storage1130 may include simulation software 1120 and/or hardware or softwaremodel 1110. In one embodiment, the data representing the IP core designmay be provided to storage 1130 via memory 1140 (e.g., hard disk), wiredconnection (e.g., internet) 1150 or wireless connection 1160. The IPcore information generated by the simulation tool and model may then betransmitted to a fabrication facility where it may be fabricated by a3^(rd) party to perform at least one instruction in accordance with atleast one embodiment.

In some embodiments, one or more instructions may correspond to a firsttype or architecture (e.g., x86) and be translated or emulated on aprocessor of a different type or architecture (e.g., ARM). Aninstruction, according to one embodiment, may therefore be performed onany processor or processor type, including ARM, x86, MIPS, a GPU, orother processor type or architecture.

FIG. 12 illustrates how an instruction of a first type may be emulatedby a processor of a different type, in accordance with embodiments ofthe present disclosure. In FIG. 12, program 1205 contains someinstructions that may perform the same or substantially the samefunction as an instruction according to one embodiment. However theinstructions of program 1205 may be of a type and/or format that isdifferent from or incompatible with processor 1215, meaning theinstructions of the type in program 1205 may not be able to executenatively by the processor 1215. However, with the help of emulationlogic, 1210, the instructions of program 1205 may be translated intoinstructions that may be natively be executed by the processor 1215. Inone embodiment, the emulation logic may be embodied in hardware. Inanother embodiment, the emulation logic may be embodied in a tangible,machine-readable medium containing software to translate instructions ofthe type in program 1205 into the type natively executable by processor1215. In other embodiments, emulation logic may be a combination offixed-function or programmable hardware and a program stored on atangible, machine-readable medium. In one embodiment, the processorcontains the emulation logic, whereas in other embodiments, theemulation logic exists outside of the processor and may be provided by athird party. In one embodiment, the processor may load the emulationlogic embodied in a tangible, machine-readable medium containingsoftware by executing microcode or firmware contained in or associatedwith the processor.

FIG. 13 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to embodimentsof the invention. In the illustrated embodiment, the instructionconverter is a software instruction converter, although alternativelythe instruction converter may be implemented in software, firmware,hardware, or various combinations thereof. FIG. 13 shows a program in ahigh level language 1302 may be compiled using an x86 compiler 1304 togenerate x86 binary code 1306 that may be natively executed by aprocessor with at least one x86 instruction set core 1316. The processorwith at least one x86 instruction set core 1316 represents any processorthat can perform substantially the same functions as an Intel processorwith at least one x86 instruction set core by compatibly executing orotherwise processing (1) a substantial portion of the instruction set ofthe Intel x86 instruction set core or (2) object code versions ofapplications or other software targeted to run on an Intel processorwith at least one x86 instruction set core, in order to achievesubstantially the same result as an Intel processor with at least onex86 instruction set core. The x86 compiler 1304 represents a compilerthat is operable to generate x86 binary code 1306 (e.g., object code)that can, with or without additional linkage processing, be executed onthe processor with at least one x86 instruction set core 1316.Similarly, FIG. 13 shows the program in the high level language 1302 maybe compiled using an alternative instruction set compiler 1308 togenerate alternative instruction set binary code 1310 that may benatively executed by a processor without at least one x86 instructionset core 1314 (e.g., a processor with cores that execute the MIPSinstruction set of MIPS Technologies of Sunnyvale, Calif. and/or thatexecute the ARM instruction set of ARM Holdings of Sunnyvale, Calif.).

The instruction converter 1312 is used to convert the x86 binary code1306 into alternative instruction set binary code 1311 that may benatively executed by the processor without an x86 instruction set core1314. This converted code may or may not be the same as the alternativeinstruction set binary code 1310 resulting from an alternativeinstruction set compiler 1308; however, the converted code willaccomplish the same general operation and be made up of instructionsfrom the alternative instruction set. Thus, the instruction converter1312 represents software, firmware, hardware, or a combination thereofthat, through emulation, simulation or any other process, allows aprocessor or other electronic device that does not have an x86instruction set processor or core to execute the x86 binary code 1306.

FIG. 14 is a block diagram of an instruction set architecture 1400 of aprocessor, in accordance with embodiments of the present disclosure.Instruction set architecture 1400 may include any suitable number orkind of components.

For example, instruction set architecture 1400 may include processingentities such as one or more cores 1406, 1407 and a graphics processingunit 1415. Cores 1406, 1407 may be communicatively coupled to the restof instruction set architecture 1400 through any suitable mechanism,such as through a bus or cache. In one embodiment, cores 1406, 1407 maybe communicatively coupled through an L2 cache control 1408, which mayinclude a bus interface unit 1409 and an L2 cache 1410. Cores 1406, 1407and graphics processing unit 1415 may be communicatively coupled to eachother and to the remainder of instruction set architecture 1400 throughinterconnect 1410. In one embodiment, graphics processing unit 1415 mayuse a video code 1420 defining the manner in which particular videosignals will be encoded and decoded for output.

Instruction set architecture 1400 may also include any number or kind ofinterfaces, controllers, or other mechanisms for interfacing orcommunicating with other portions of an electronic device or system.Such mechanisms may facilitate interaction with, for example,peripherals, communications devices, other processors, or memory. In theexample of FIG. 14, instruction set architecture 1400 may include aliquid crystal display (LCD) video interface 1425, a subscriberinterface module (SIM) interface 1430, a boot ROM interface 1435, asynchronous dynamic random access memory (SDRAM) controller 1440, aflash controller 1445, and a serial peripheral interface (SPI) masterunit 1450. LCD video interface 1425 may provide output of video signalsfrom, for example, GPU 1415 and through, for example, a mobile industryprocessor interface (MIPI) 1490 or a high-definition multimediainterface (HDMI) 1495 to a display. Such a display may include, forexample, an LCD. SIM interface 1430 may provide access to or from a SIMcard or device. SDRAM controller 1440 may provide access to or frommemory such as an SDRAM chip or module. Flash controller 1445 mayprovide access to or from memory such as flash memory or other instancesof RAM. SPI master unit 1450 may provide access to or fromcommunications modules, such as a Bluetooth module 1470, high-speed 3Gmodem 1475, global positioning system module 1480, or wireless module1485 implementing a communications standard such as 802.11.

FIG. 15 is a more detailed block diagram of an instruction setarchitecture 1500 of a processor, in accordance with embodiments of thepresent disclosure. Instruction architecture 1500 may implement one ormore aspects of instruction set architecture 1400. Furthermore,instruction set architecture 1500 may illustrate modules and mechanismsfor the execution of instructions within a processor.

Instruction architecture 1500 may include a memory system 1540communicatively coupled to one or more execution entities 1565.Furthermore, instruction architecture 1500 may include a caching and businterface unit such as unit 1510 communicatively coupled to executionentities 1565 and memory system 1540. In one embodiment, loading ofinstructions into execution entities 1564 may be performed by one ormore stages of execution. Such stages may include, for example,instruction prefetch stage 1530, dual instruction decode stage 1550,register rename stage 155, issue stage 1560, and writeback stage 1570.

In another embodiment, memory system 1540 may include a retirementpointer 1582. Retirement pointer 1582 may store a value identifying theprogram order (PO) of the last retired instruction. Retirement pointer1582 may be set by, for example, retirement unit 454. If no instructionshave yet been retired, retirement pointer 1582 may include a null value.

Execution entities 1565 may include any suitable number and kind ofmechanisms by which a processor may execute instructions. In the exampleof FIG. 15, execution entities 1565 may include ALU/multiplication units(MUL) 1566, ALUs 1567, and floating point units (FPU) 1568. In oneembodiment, such entities may make use of information contained within agiven address 1569. Execution entities 1565 in combination with stages1530, 1550, 1555, 1560, 1570 may collectively form an execution unit.

Unit 1510 may be implemented in any suitable manner. In one embodiment,unit 1510 may perform cache control. In such an embodiment, unit 1510may thus include a cache 1525. Cache 1525 may be implemented, in afurther embodiment, as an L2 unified cache with any suitable size, suchas zero, 128 k, 256 k, 512 k, 1M, or 2M bytes of memory. In another,further embodiment, cache 1525 may be implemented in error-correctingcode memory. In another embodiment, unit 1510 may perform businterfacing to other portions of a processor or electronic device. Insuch an embodiment, unit 1510 may thus include a bus interface unit 1520for communicating over an interconnect, intraprocessor bus,interprocessor bus, or other communication bus, port, or line. Businterface unit 1520 may provide interfacing in order to perform, forexample, generation of the memory and input/output addresses for thetransfer of data between execution entities 1565 and the portions of asystem external to instruction architecture 1500.

To further facilitate its functions, bus interface unit 1520 may includean interrupt control and distribution unit 1511 for generatinginterrupts and other communications to other portions of a processor orelectronic device. In one embodiment, bus interface unit 1520 mayinclude a snoop control unit 1512 that handles cache access andcoherency for multiple processing cores. In a further embodiment, toprovide such functionality, snoop control unit 1512 may include acache-to-cache transfer unit that handles information exchanges betweendifferent caches. In another, further embodiment, snoop control unit1512 may include one or more snoop filters 1514 that monitors thecoherency of other caches (not shown) so that a cache controller, suchas unit 1510, does not have to perform such monitoring directly. Unit1510 may include any suitable number of timers 1515 for synchronizingthe actions of instruction architecture 1500. Also, unit 1510 mayinclude an AC port 1516.

Memory system 1540 may include any suitable number and kind ofmechanisms for storing information for the processing needs ofinstruction architecture 1500. In one embodiment, memory system 1504 mayinclude a load store unit 1530 for storing information such as bufferswritten to or read back from memory or registers. In another embodiment,memory system 1504 may include a translation lookaside buffer (TLB) 1545that provides look-up of address values between physical and virtualaddresses. In yet another embodiment, bus interface unit 1520 mayinclude a memory management unit (MMU) 1544 for facilitating access tovirtual memory. In still yet another embodiment, memory system 1504 mayinclude a prefetcher 1543 for requesting instructions from memory beforesuch instructions are actually needed to be executed, in order to reducelatency.

The operation of instruction architecture 1500 to execute an instructionmay be performed through different stages. For example, using unit 1510instruction prefetch stage 1530 may access an instruction throughprefetcher 1543. Instructions retrieved may be stored in instructioncache 1532. Prefetch stage 1530 may enable an option 1531 for fast-loopmode, wherein a series of instructions forming a loop that is smallenough to fit within a given cache are executed. In one embodiment, suchan execution may be performed without needing to access additionalinstructions from, for example, instruction cache 1532. Determination ofwhat instructions to prefetch may be made by, for example, branchprediction unit 1535, which may access indications of execution inglobal history 1536, indications of target addresses 1537, or contentsof a return stack 1538 to determine which of branches 1557 of code willbe executed next. Such branches may be possibly prefetched as a result.Branches 1557 may be produced through other stages of operation asdescribed below. Instruction prefetch stage 1530 may provideinstructions as well as any predictions about future instructions todual instruction decode stage.

Dual instruction decode stage 1550 may translate a received instructioninto microcode-based instructions that may be executed. Dual instructiondecode stage 1550 may simultaneously decode two instructions per clockcycle. Furthermore, dual instruction decode stage 1550 may pass itsresults to register rename stage 1555. In addition, dual instructiondecode stage 1550 may determine any resulting branches from its decodingand eventual execution of the microcode. Such results may be input intobranches 1557.

Register rename stage 1555 may translate references to virtual registersor other resources into references to physical registers or resources.Register rename stage 1555 may include indications of such mapping in aregister pool 1556. Register rename stage 1555 may alter theinstructions as received and send the result to issue stage 1560.

Issue stage 1560 may issue or dispatch commands to execution entities1565. Such issuance may be performed in an out-of-order fashion. In oneembodiment, multiple instructions may be held at issue stage 1560 beforebeing executed. Issue stage 1560 may include an instruction queue 1561for holding such multiple commands. Instructions may be issued by issuestage 1560 to a particular processing entity 1565 based upon anyacceptable criteria, such as availability or suitability of resourcesfor execution of a given instruction. In one embodiment, issue stage1560 may reorder the instructions within instruction queue 1561 suchthat the first instructions received might not be the first instructionsexecuted. Based upon the ordering of instruction queue 1561, additionalbranching information may be provided to branches 1557. Issue stage 1560may pass instructions to executing entities 1565 for execution.

Upon execution, writeback stage 1570 may write data into registers,queues, or other structures of instruction set architecture 1500 tocommunicate the completion of a given command. Depending upon the orderof instructions arranged in issue stage 1560, the operation of writebackstage 1570 may enable additional instructions to be executed.Performance of instruction set architecture 1500 may be monitored ordebugged by trace unit 1575.

FIG. 16 is a block diagram of an execution pipeline 1600 for aninstruction set architecture of a processor, in accordance withembodiments of the present disclosure. Execution pipeline 1600 mayillustrate operation of, for example, instruction architecture 1500 ofFIG. 15.

Execution pipeline 1600 may include any suitable combination of steps oroperations. In 1605, predictions of the branch that is to be executednext may be made. In one embodiment, such predictions may be based uponprevious executions of instructions and the results thereof. In 1610,instructions corresponding to the predicted branch of execution may beloaded into an instruction cache. In 1615, one or more such instructionsin the instruction cache may be fetched for execution. In 1620, theinstructions that have been fetched may be decoded into microcode ormore specific machine language. In one embodiment, multiple instructionsmay be simultaneously decoded. In 1625, references to registers or otherresources within the decoded instructions may be reassigned. Forexample, references to virtual registers may be replaced with referencesto corresponding physical registers. In 1630, the instructions may bedispatched to queues for execution. In 1640, the instructions may beexecuted. Such execution may be performed in any suitable manner. In1650, the instructions may be issued to a suitable execution entity. Themanner in which the instruction is executed may depend upon the specificentity executing the instruction. For example, at 1655, an ALU mayperform arithmetic functions. The ALU may utilize a single clock cyclefor its operation, as well as two shifters. In one embodiment, two ALUsmay be employed, and thus two instructions may be executed at 1655. At1660, a determination of a resulting branch may be made. A programcounter may be used to designate the destination to which the branchwill be made. 1660 may be executed within a single clock cycle. At 1665,floating point arithmetic may be performed by one or more FPUs. Thefloating point operation may require multiple clock cycles to execute,such as two to ten cycles. At 1670, multiplication and divisionoperations may be performed. Such operations may be performed in fourclock cycles. At 1675, loading and storing operations to registers orother portions of pipeline 1600 may be performed. The operations mayinclude loading and storing addresses. Such operations may be performedin four clock cycles. At 1680, write-back operations may be performed asrequired by the resulting operations of 1655-1675.

FIG. 17 is a block diagram of an electronic device 1700 for utilizing aprocessor 1710, in accordance with embodiments of the presentdisclosure. Electronic device 1700 may include, for example, a notebook,an ultrabook, a computer, a tower server, a rack server, a blade server,a laptop, a desktop, a tablet, a mobile device, a phone, an embeddedcomputer, or any other suitable electronic device.

Electronic device 1700 may include processor 1710 communicativelycoupled to any suitable number or kind of components, peripherals,modules, or devices. Such coupling may be accomplished by any suitablekind of bus or interface, such as I²C bus, system management bus(SMBus), low pin count (LPC) bus, SPI, high definition audio (HDA) bus,Serial Advance Technology Attachment (SATA) bus, USB bus (versions 1, 2,3), or Universal Asynchronous Receiver/Transmitter (UART) bus.

Such components may include, for example, a display 1724, a touch screen1725, a touch pad 1730, a near field communications (NFC) unit 1745, asensor hub 1740, a thermal sensor 1746, an express chipset (EC) 1735, atrusted platform module (TPM) 1738, BIOS/firmware/flash memory 1722, adigital signal processor 1760, a drive 1720 such as a solid state disk(SSD) or a hard disk drive (HDD), a wireless local area network (WLAN)unit 1750, a Bluetooth unit 1752, a wireless wide area network (WWAN)unit 1756, a global positioning system (GPS), a camera 1754 such as aUSB 3.0 camera, or a low power double data rate (LPDDR) memory unit 1715implemented in, for example, the LPDDR3 standard. These components mayeach be implemented in any suitable manner.

Furthermore, in various embodiments other components may becommunicatively coupled to processor 1710 through the componentsdiscussed above. For example, an accelerometer 1741, ambient lightsensor (ALS) 1742, compass 1743, and gyroscope 1744 may becommunicatively coupled to sensor hub 1740. A thermal sensor 1739, fan1737, keyboard 1746, and touch pad 1730 may be communicatively coupledto EC 1735. Speaker 1763, headphones 1764, and a microphone 1765 may becommunicatively coupled to an audio unit 1764, which may in turn becommunicatively coupled to DSP 1760. Audio unit 1764 may include, forexample, an audio codec and a class D amplifier. A SIM card 1757 may becommunicatively coupled to WWAN unit 1756. Components such as WLAN unit1750 and Bluetooth unit 1752, as well as WWAN unit 1756 may beimplemented in a next generation form factor (NGFF).

Embodiments of the present disclosure involve an instruction and logicfor a vector format for processing computations. In one embodiment, suchcomputations may include finite-difference computations. For example,such computations may include multi-dimensional differential equationcalculations or estimations, isotropic computations, or anisotropiccomputations. In another embodiment, such a vector format may include atabular vector format. In yet another embodiment, the computations mayinclude computations requiring input from multiple, overlapping datapoints that are adjacent to each other in multiple dimensions. FIG. 18is a block diagram of an example embodiment of a system 1800 for avector format for processing computations, according to embodiments ofthe present disclosure. Computations may be processed by, for example,processor 1804. System 1800 may include any suitable number and kind ofelements to perform the functionality described herein. Furthermore,although specific elements of system 1800 may be described herein asperforming a specific function, any suitable portion of system 1800 mayperform the functionality described herein.

In one embodiment, vector formats for processing computations may beapplied to instructions received by processor 1804. The instructionsreceived by processor 1804 may include those in instruction stream 1802,which may be generated by a compiler, translator, or other suitablesource. Instruction stream 1804 may include a command for afinite-difference (FD) computation or function, such as an n-dimensionalisotropic, anisotropic, or differential equation function. Instructionstream 1804 may include a command for a function that requires, for aparticular data point, input from multiple data points that are adjacentto each other linearly and in multiple dimensions. In variousembodiments, the “multiple dimensions” may represent the physicalreality or domain of the problem space represented by source data. Whenmanipulated and processed within processor 1804, multiple domains may beflattened as necessary into real data structures in order to performcomputer operations. The command may be referenced as an FD function1803. In such an embodiment, processor 1804 may implement execution ofFD function 1803 by decoding, interpreting, or otherwise performing FDfunction 1803 by adding vector formatting operations. Such vectorformatting operations may be performed by any suitable portion ofprocessor 1804, such as by front end 1806, decoder 1808, or dynamicbinary translator 1816, or other elements not shown such as ajust-in-time compiler or translator, optimizer, or a specialco-processor or execution unit. In another embodiment, vector formatsfor processing computations may be applied to FD function 1803 beforeinstructions arrive at processor 1804. The vector formatting may havebeen applied by a compiler, translator, optimizer, or other suitableentity.

Processor 1802 may perform processing of FD function 1803, its vectorformatting operations, as well as other instructions. Processor 1804 maybe implemented in part by any processor core, logical processor,processor, or other processing entity such as those illustrated in FIGS.1-17. In various embodiments, processor 1804 may include a front end1806 including a fetch unit to fetch instructions from cache, memory, orother sources and a decoder 1808 to decode instructions. Processor 1804may also include a scheduler to determine timing of instructions, orderof instructions, and assignment of instructions to cores 1814 orexecution units 1820. Processor 1804 may also include as many types andkinds of execution units 1820 or cores 1814 to execute instructions.Such execution units may include, for example, branch execution units,integer arithmetic execution units (e.g., ALUs) floating pointarithmetic execution units (e.g., FPUs) and memory access executionunits. Furthermore, processor 1804 may include a retirement unit 1816 tocommit the results of successful execution to, for example, registers,cache, or memory 1818. Processor 1804 may include any other suitablecomponents that are not shown, such as allocation units to reserve aliasresources. In various embodiments, processor 1804 may utilize microcodeto execute utilizing micro-operations stored on a non-volatile machinereadable medium (such as a Read-Only-Memory) within the die of processor1802 to cause execution units 1820 to perform the desired operation ofthe instruction.

As discussed above, FD function 1803 may include vector formattingoperations to implement FD function 1803 in instruction stream 1802 oras the result of interpreting, decoding, or otherwise evaluating FDfunction 1803 in processor 1804. Any suitable vector formattingoperations, reads, or other instructions may be added to instructionstream 1802 or decoded and added to FD function 1803 as it is to beexecuted in processor 1804. The particular selection and order offormatting operations, reads, or other instructions may be made toimplement the execution of the particular FD function 1803 and itsparticular parameters. In one embodiment, such formatting operations mayinclude functions to read in data into tabular vectors. In anotherembodiment, such formatting operations may include functions to aligntabular vectors for execution according to the needs of a particular FDfunction 1803 and its parameters. In yet another embodiment, suchformatting operations may include functions to permute tabular vectorsfor execution according to the needs of a particular FD function 1803and its parameters.

As FD function 1803 arrives at processor 1804, it might not haveincluded such formatting operations to specifically implement itsexecution, in one embodiment. In another embodiment, FD function 1803may have been compiled and such formatting operations added to it beforebeing placed into instruction stream 1802.

As described above, the function of FD function 1803 may include, forexample, an n-dimensional FD, isotropic, anisotropic, or differentialequation approximation function. FD functions may include numericalschemes for the solution of differential equations. In anotherembodiment, a function may be used which requires, for a single point ofcalculation, input from data points that are adjacent to each other inmultiple dimensions. Any of these functions may be used in simulation oflarge sets of data. Such functions may be used in high-performancecomputing to simulate, for example, geological formations in energyexploration, or any other suitable application. Implementation of thesefunctions may require an extremely large amount of floating pointcalculations and high memory bandwidths. FD functions may require theapplication of a numerical stencil across a large memory spacerepresenting data. Such data may include real-world data. Thus,calculation of a single point in an FD function may require accessingmultiple pieces of data in each dimension. For each dimension of FDfunction 1803, FD function may include ranges of input values that arenecessary for calculating its result at a single point. Such rangesmight not be contiguous. For example, a 16th order two-dimensionalstencil may require thirty-three input values to calculate each newvalue in a single time step. Solution of an entire FD-based problem mayrequire billions of points to be calculated. SIMD vectorization inprocessor 1804 may allow multiple points to be computed in parallel.However, as shown below, SIMD vectorization does not reduce the datarequired to be read, and alone might not similarly impact the amount ofreads that must be made to perform FD function execution. In variousembodiments, the vector formatting applied to FD function 1803 mayreduce the number of reads needed to gather data for execution by FDfunction 1803.

FIG. 18 illustrates an example three-dimensional source data 1822. Suchdata 1822 may be resident in any suitable place, including in memory orin another location accessible by processor 1804. Data 1822 may be toolarge to be completely read and stored within cache of processor 1804.FD function 1803 may process data 1822 to determine results of theintended function. As discussed above, calculation of FD function 1803at a given location (x_(o), y_(o), z_(o)) may require inputs from ranges(contiguous or otherwise) of source data 1822 from each of thedimensions x, y, and z. In order to obtain such inputs, at (A) each suchvalue from source data 1822 must be read. At (B), such values may beused to calculate a resultant value of FD function 1803. In oneembodiment, multiple values of FD function 1803 may be calculated inparallel using SIMD vectorization operations.

FIG. 19 is an illustration of example FD functions, in accordance withembodiments of the present disclosure. Stencil 1902 is a representationof an example FD function that, for a given point 1903, requiresdetermining the values of the next four and previous four values in thex-direction, the next four and previous four values in the y-direction,and the next four and previous four values in the z-direction. The mapof values necessary to calculate the given point 1803 of the FD functionmay be referred to as a mask or stencil. Example formulas 1904 forisotropic functions are also specified in FIG. 19.

FIG. 20 is an illustration of example operation of an FD function, inaccordance with embodiments of the present disclosure. Graph 2002illustrates a single point at (5, 5) for which execution of FD function1803 might be made. The single point might be only one of millions ormore points for which calculations by FD function 1803 are to be made.

Graph 2004 illustrates a stencil 2006 illustrating the values that mustbe read in order to calculate a two-dimensional 8th order isotropic FDfunction. Larger stencils would be necessary for a three-dimensionalfunction, or for a higher order (such as 16th order) function. For thetwo-dimensional 8th order isotropic FD function considered in FIG. 20,the given point, the four values above, the four values below, the fourvalues to the right, and the four values to the left of the given pointin the source data may be considered to find the result of the FDfunction.

In order to read these values of stencil 2006, in (A) a read 2003 ofindividual data points might be made, requiring a total of seventeenreads to determine the values necessary for the calculation of (5,5) forthe FD function. Using an SIMD vector read 2005 in (B), more than onevalue might be read in at a time into a vector of values. Such an SIMDvector might be defined according to the architecture and features ofprocessor 1804. For example, an SIMD vector read of a length of sixteenmight be available, each with consecutive indices in the x-direction.The values read may correspond to the location specified, as well as thefifteen values that follow it in the direction of the vector. Using SIMDvector read 2005, the values in the source data corresponding to stencil2006 might be read in eleven vector reads. If the example FD functionwere three dimensional, another four reads would be made for the valuescoming out of the page (in the z-direction) as well as another fourreads going into the page. Furthermore, more data may be gathered bythese reads such that simultaneous execution of vectors in parallel maybe facilitated. The first nine such reads are illustrated in graph 2006,the tenth read illustrated in graph 2008, and the eleventh readillustrated in graph 2010. For a 16th order isotropic 2-D FD function,thirty-three input data values may be necessary. Nineteen differentexecutions of SIMD vector read 2005 may be used to input such data. Asvectorization is done in a single direction, efficiency may be gained inthe direction of the vectorization (in the example of graphs 2006, 2008,2010, the x-direction), but multiple reads must be made in the otherdirections.

Although SIMD vector read 2005 may include a possible read of sixteenvalues in parallel, SIMD vector read 2005 might not be capable ofreading in any possible set of sixteen values at once. SIMD vector read2005 might be able to read in parallel sixteen elements that are alignedwithin a single cache line. In the example of graph 2006, one alignmentmight be on column five, while the next alignment might be on columntwenty-one. An SIMD vector read 2005 might read such a range in onechunk. Accordingly, the read of the left “arm” in graph 2006 might nowshow the full alignment that is actually employed by processors. Such aread may in fact be at column negative-eleven and go through columnfour. Consequently, nineteen different reads may be necessary tosufficiently cover the stencil.

In one embodiment, vector formatting may be applied to reading data forexecution of FD functions such that data is vectorized in more than onedimension, such as in two-dimensions or three-dimensions. Suchformatting may include a tabular vector read 2011 format such as shownat (C). In another embodiment, vector formatting may be applied in morethan one dimension so that data is read in more than one dimension.

Tabular vector read 2011 format may be applied to the source data andstencil 2006 in any suitable manner. In one embodiment, tabular vectorread 2011 may be applied to a given point wherein the next four valuesin the x-direction and the next four values in the y-direction may beread. The size of tabular vector read 2011 may be related to theavailable size of SIMD vector read 2005 for processor 1804. For example,tabular vector read 2011 may be sixteen values. If a vector read ofsixty-four values were supported, tabular vector read 2011 might includean eight-by-eight two-dimensional read. If a vector read of anon-perfect square (such as thirty-two values) were supported, tabularvector read 2011 might be implemented by, for example, an eight-by-fourtwo-dimensional read. Any suitable layout of tabular vector read 2011may be used. For example, a two-by-eight layout may be used instead ofthe four-by-four layout illustrated in FIG. 20. In another example, thesixteen-element SIMD may be used to represent a four-by-two-by-twosubspace in a three-dimensional read. The layout selected to be appliedto source data may depend upon the shape of the source data needed toexecute the FD function.

In another embodiment, tabular vector read 2011 may be applied to thepoints in stencil 2006 (or any other stencil for the FD function inquestion) so as to minimize the number of reads while fully reading allvalues of stencil 2006, given the dimensions of tabular vector read2011. How to apply the vector reads may be determined by, for example,processor 1804 in decoding and translating FD function 1803, or may beincluded with FD function 1803.

For example, graph 2012 illustrates application of tabular vector read2011 to stencil 2006 representing an example FD function. Theinstruction for FD function may be received by processor 1804 andprocessor 1804 may determine that, in order to perform calculation of FDfunction, the values of stencil 2006 must be read. Furthermore,processor 1804 may determine that, in order to efficiently read thevalues of stencil 2006, five instances of tabular vector read 2011 mightbe made. The parameters of tabular vector read 2011 in the fiveinstances may be selected so as to produce the reads illustrated ingraph 2012. For example, a tabular vector read 2011 may be made at eachof (5, 2), (1, 6), (5, 6), (5, 10), and (9, 6) in the source data tocalculate the FD function result for (5, 6). The specific number ofvector reads may depend upon the given point chosen, the FD function,the order of the FD function, the number of dimensions of the FDfunction. Mappings of the relationships between where such reads may beperformed and such variations of FD functions may be stored such that,given a particular FD function, the specific number and location oftabular vector read 2011 may be specified in order to implement thereads of the particular FD function. Furthermore, such mappings mayspecify various available layouts of tabular vector read 2011.

Thus, in the example of FIG. 20, five reads may be needed to read allvalues for the 8th order 2-D FD function and its stencil 2006. For athree-dimensional function, an additional four reads would be needed forthe values in the z-direction appearing out of the page as well as anadditional four reads for the values in the z-direction going in to thepage. In the case of the 16th order 2-D FD function, the number oftabular vector reads 2011 may be nine. Accordingly, in variousembodiments, formatting of vector reads from linear vectors to tabularvectors may reduce the number of reads of source data needed tocalculate FD function 1803.

FIG. 21 is an illustration of example operation of an anisotropicfunction 2102, in accordance with embodiments of the present disclosure.Although anisotropic function 2102 is used an example in FIG. 22, anysuitable anisotropic function may be used. Anisotropic function 2102 maybe a two-dimensional anisotropic function.

Graph 2104 illustrates a single point at (5, 5) for which execution ofanisotropic function 2102 might be made. The single point might be onlyone of millions or more points for which calculations by anisotropicfunction 2102 are to be made. Graph 2104 illustrates a stencil 2106illustrating the values that must be read in order to calculate a valueof the given point using anisotropic function 2102. Larger stencilswould be necessary for a three-dimensional function, or for a higherorder (such as 16th order) function. For the two-dimensional 8th orderisotropic function considered in FIG. 21, the given point, the fourvalues above, the four values below, the four values to the right, andthe four values to the left of the given point in the source data may beconsidered to find the result of anisotropic function 2102 for the givenpoint. In addition, the four values diagonally above and to the right,the four values diagonally above and to the left, the four valuesdiagonally below and to the right, and the four values diagonally belowand to the left may be considered to find the result of anisotropicfunction 2102 for the given point.

In order to read these values of stencil 2106, if a linear SIMD vectorread (such as 1×16 SIMD vector read 2005 from FIG. 20) is used to readvalues to make the calculation, a total of twenty-seven reads might needto be made. This total may include the same as those shown in FIG. 20 aswell as four for each of the four diagonal regions.

In one embodiment, vector formatting may be applied to reading data forexecuting anisotropic function 2102 such that data is vectorized in morethan one dimension, such as in two-dimensions or three-dimensions. Suchformatting may include the tabular vector read 2011 format shown in FIG.20. In another embodiment, vector formatting may be applied in more thanone dimension so that data is read in more than one dimension.

Tabular vector read 2011 format may be applied to the source data andstencil 2106 in any suitable manner. Tabular vector read 2011 may beapplied to the points in stencil 2106 so as to minimize the number ofreads while fully reading all values of stencil 2206. How to apply thevector reads may be determined by, for example, processor 1804 indecoding and translating anisotropic function 2102, or may be includedwith anisotropic function 2102.

For example, graph 2108 illustrates application of tabular vector read2011 to stencil 2106 representing anisotropic function 2102. In oneembodiment, tabular vector read 2011 may be applied to points (1, 2),(5, 2), (9, 2), (1, 6), (5, 6), (9, 10), (1, 10), (5, 10), and (9, 10)as shown in graph 2108. Accordingly, nine reads may be made. For athree-dimensional function, an additional four reads would be needed forthe values in the z-direction appearing out of the page as well as anadditional four reads for the values in the z-direction going in to thepage. In the case of the 16th order 2-D FD function, the number oftabular vector reads 2011 may be nine. Accordingly, in variousembodiments, formatting of vector reads from linear vectors to tabularvectors may reduce the number of reads of source data needed tocalculate FD function 1803.

FIG. 22 is an illustration of example operation of system 1800 to makecalculations based upon tabular vector reads, in accordance withembodiments of the present disclosure. Execution of calculations of FDfunction 1803 may be performed in parallel using SIMD vectorization.Vector data in FIG. 22 may be stored in, for example, vector registers.The parallelization of calculations may be made by performingcalculations using the same tabular vector layout used in for readsshown in FIG. 20. Thus, the number of calculations performed in parallelusing vector calculation in FIG. 22 may be the same as the size of thetabular vectors read operations in FIG. 20. Moreover, the number ofcalculations performed in parallel using vector calculations may be thesame size as registers storing the results of the tabular vectors thatstore the results of the tabular vector read operations.

However, a consideration of a given point using the tabular vectorlayout used to read the data may require accessing data retrieved duringtwo different tabular vector reads. If the vector calculation is thesame size of the registers storing data of a single tabular vector read,then vector calculation might not be able to read all of both of thetabular vector reads. In some embodiments, formatting instructions maybe applied to correctly gather information from multiple vectors so thatcalculations may be made according to a tabular vector layout 2206.Layout 2206 may correspond to the layout of tabular vector readsillustrated in FIG. 20.

For example, graph 2202 may illustrate data received from a first vectorof data (Vector1) and a second vector of data (Vector2) generated fromtabular vector reads. In graph 2204, in order to perform vector-widecalculations on, for example, location (5, 5), which is one locationremoved in the y-direction from the center point of stencil 1006, datafrom both Vector1 and Vector2 might need to be accessed. Accordingly,the data might need to be formatted so that vector-wide calculationmight be performed for location (5, 5). Notably, additional informationmight be required for such a calculation.

In one embodiment, in order to access information from multiple tabularvectors, a specialized vector read function may be used. In the exampleof FIG. 22, such a function may be denoted as VALIGN. In one embodiment,VALIGN may be implemented as a single instruction. VALIGN may use anysuitable parameters to specify that two portions from two vectors shouldbe joined. For example, VALIGN may include a parameter specifying thefirst vector, what elements from the first vector should be taken, thesecond vector, and what elements from the second vector should be taken.VALIGN may be implemented with other suitable schemes of parameters. Inthe example of graph 2204, VALIGN may be called to designate that thelast four elements of Vector1 (specified by negative four) are to beadded to the first twelve elements of Vector2 (specified by twelve):VALIGN (Vector1, −4, Vector 2, 12). In another example, the negativevalue may be implied. In another example, the relationship betweenVector1 and Vector2 may be inferred, and only a single number passedindicating how many of the last values of Vector1 are to be used. Theremaining values may be assumed to be from the first portion of Vector2.

In the example of graph 2204, to complete an additional calculation forlayout 2206 shifted to be at (5, 3) (one more position up), VALIGN maybe called using VALIGN(Vector1, −8, Vector2, 8) such that the last eightvalues of Vector1 are combined with the first eight values of Vector2.For an additional calculation for layout 2206 instead placed at (5, 2)(another position up), VALIGN may be called using VALIGN(Vector1, −12,Vector2, 4) such that the last twelve values of Vector1 are combinedwith the first four values of Vector2. However, for an additionalcalculation for layout 2206 instead placed at (5, 1) (another positionup), layout 2206 may be completed directed to Vector1 data. In such acase, use of VALIGN might not be needed, and Vector1 data might simplybe used as it was read.

In one embodiment, for calculations in the vector below Vector2, thesame steps may be repeated using different parameters of VALIGN tocombine elements of such a vector with Vector2. A total of six VALIGNcalls might be used to perform all the calculations for the tabularvector data as applied by layout 2206 in the y-direction. The six VALIGNcalls may include three for mixes of the “top” “arm” and middle vector,and three for mixes of the “bottom” arm and middle vector of graph 2202.Accessing the entire arm at the top for Vector1 might not require aVALIGN call. Accessing the entire middle Vector2 might not require aVALIGN call. Accessing the entire “bottom” arm corresponding to anothervector might not require a VALIGN call. In a 16th-order stencil, twoadditional input vectors and six more VALIGN calls might be necessary.

Once a given layout is read, sixteen results might be calculated inparallel using SIMD vector calculation in tabular form.

Graph 2210 illustrates resultant tabular vectors of data for Vector3 inthe “left” arm and for Vector2. To perform similar calculations in thex-direction as those that were performed in the y-direction above,layout 2206 may be shifted from Vector2 to the left in the x-directionand the components therein calculated. In order to access data from bothVector3 and Vector2, VALIGN may be used in a manner similar to thatdescribed above. In one embodiment, tabular vector computations andVALIGN may be performed presuming that layout 2206 presents values asshown in tabular vector read 2011 format. In another embodiment,calculations for shifting locations of layout 2206 in the x-directionmay be made by first permuting or transposing the contents of layout2206, so that calculations and VALIGN operations are performed as ifthey were done from the y-direction perspective. Accordingly, Vector3and Vector2 may be permuted or transposed. In another embodiment, VALIGNoperations may then be applied to join elements of Vector3 and Vector2,upon which calculations may be made. The final results may need to betransposed before being written to memory. The permuting operation maybe illustrated in operation 2212.

After Vector3 and Vector2 are permuted, VALIGN calls may be made to jointhe last four elements of Vector3 with the first twelve elements ofVector2; the last eight elements of Vector3 with the first eightelements of Vector2; and the last twelve elements of Vector3 with thefirst four elements of Vector2. VALIGN might not be needed to access theleft arm. The process may be repeated with the “right” arm, includingpermuting the tabular vector therein. Upon assembling the data in eachlayout, SIMD tabular vector calculation may be performed.

In an 8th order stencil, three permute operations might be needed (onefor each tabular vector in the x-direction), as well as six VALIGN callsin total to cover the mixes of the arms and the center. For a 16th orderstencil, two additional input vectors might be needed, as well as twomore permutes and six more VALIGN calls.

FIG. 23 is a flowchart of an example embodiment of a method 2300 forapplying a vector format for processing computations, in accordance withembodiments of the present disclosure. Method 2300 may illustrateoperations performed by, for example, processor 1804. Some portions ofmethod 2300 may be performed by creation of instruction stream 1802 by,for example, a library, compiler, or interpreter. Method 2300 may beginat any suitable point and may execute in any suitable order. In oneembodiment, method 2300 may begin at 2305.

At 2305, an instruction may be fetched to be executed on processor 1804.Such an instruction may include a form of an FD instruction. At 2310,the instruction may be decoded. The particular type of FD instruction,and its parameters, might be determined.

In one embodiment, at 2315 a tabular vector format may be determined tobe applied to the FD instruction. Based upon the FD instruction, itsorder, its dimensions, or other identifying aspects, a particular shapeof input data may be used to calculate a given data point of the FDinstruction. Based upon the shape of the input data, the form of thetabular vector and supporting instructions may be selected.

At 2320, in one embodiment a stencil of inputs reflecting the shape ofinput data for a given point of calculation of the FD instructions maybe determined. The shape may be in two or three dimensions.

At 2325, tabular vector read instructions may be inserted into code tosupport the FD instructions. In one embodiment, the tabular vector readinstructions may map to the stencil of inputs such that the stencil iscovered by the ranges of the tabular vector read instructions. Inanother embodiment, the number of tabular vector reads may be minimized.The particular number of tabular vector reads and their parameters(reflecting respective coverage by each of portions of the stencil) maydepend upon the stencil and the FD instruction itself.

At 2330, a calculation instruction may be inserted for each element ofthe resultant tabular vectors. Such calculation instructions may betabular vector executions. In one embodiment, at 2335 alignment andpermuting instructions to support given vector calculations may beinserted as necessary. A vector calculation made using data from asingle register or tabular vector (resulting from execution of the readinstructions) might not require alignment. However, a vector calculationmade using data from multiple such registers or tabular vectors mayrequire alignment to combine elements from multiple such vectors. Thespecific use of alignment instructions may be made according to theshape of data returned from the read instructions. Furthermore,permuting instructions may be inserted to perform vector calculations inadditional directions (such as the x-direction or z-direction).Permuting instructions may allow calculation to proceed in thesedirections in the same manner in which calculation was performed in theoriginal direction. The specific use of permuting instructions may bemade according to the shape of data returned from the read instructions.

At 2340, in one embodiment the instructions may be dispatched andexecuted. In one embodiment, tabular vector reads may be performed asspecified above. At 2345, alignment may be made as necessary and vectorcalculations performed. Output data may be returned to memory, cache,registers, or other suitable locations. At 2350, for other directions,permuting and alignment may be performed as necessary. Vectorcalculations may be performed. Output data may be returned. 2350 may beperformed for multiple directions.

At 2355, it may be determined whether additional data points are to becalculated in the execution of the FD instruction. If so, method 2300may return to 2320. If not, method 2300 may proceed to 2360.

At 2360, any uncommitted or unwritten data may be issued to cache,memory, registers, or other locations. At 2365, the instruction may beretired. Method 2300 may optionally repeat or terminate as needed.

Method 2300 may be initiated by any suitable criteria. Furthermore,although method 2300 describes an operation of particular elements,method 2300 may be performed by any suitable combination or type ofelements. For example, method 2300 may be implemented by the elementsillustrated in FIGS. 1-22 or any other system operable to implementmethod 2300. As such, the preferred initialization point for method 2300and the order of the elements comprising method 2300 may depend on theimplementation chosen. In some embodiments, some elements may beoptionally omitted, reorganized, repeated, or combined.

Embodiments of the mechanisms disclosed herein may be implemented inhardware, software, firmware, or a combination of such implementationapproaches. Embodiments of the disclosure may be implemented as computerprograms or program code executing on programmable systems comprising atleast one processor, a storage system (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device.

Program code may be applied to input instructions to perform thefunctions described herein and generate output information. The outputinformation may be applied to one or more output devices, in knownfashion. For purposes of this application, a processing system mayinclude any system that has a processor, such as, for example; a digitalsignal processor (DSP), a microcontroller, an application specificintegrated circuit (ASIC), or a microprocessor.

The program code may be implemented in a high level procedural or objectoriented programming language to communicate with a processing system.The program code may also be implemented in assembly or machinelanguage, if desired. In fact, the mechanisms described herein are notlimited in scope to any particular programming language. In any case,the language may be a compiled or interpreted language.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine-readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Such machine-readable storage media may include, without limitation,non-transitory, tangible arrangements of articles manufactured or formedby a machine or device, including storage media such as hard disks, anyother type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritables (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMs) such as dynamic random accessmemories (DRAMs), static random access memories (SRAMs), erasableprogrammable read-only memories (EPROMs), flash memories, electricallyerasable programmable read-only memories (EEPROMs), magnetic or opticalcards, or any other type of media suitable for storing electronicinstructions.

Accordingly, embodiments of the disclosure may also includenon-transitory, tangible machine-readable media containing instructionsor containing design data, such as Hardware Description Language (HDL),which defines structures, circuits, apparatuses, processors and/orsystem features described herein. Such embodiments may also be referredto as program products.

In some cases, an instruction converter may be used to convert aninstruction from a source instruction set to a target instruction set.For example, the instruction converter may translate (e.g., using staticbinary translation, dynamic binary translation including dynamiccompilation), morph, emulate, or otherwise convert an instruction to oneor more other instructions to be processed by the core. The instructionconverter may be implemented in software, hardware, firmware, or acombination thereof. The instruction converter may be on processor, offprocessor, or part-on and part-off processor.

Thus, techniques for performing one or more instructions according to atleast one embodiment are disclosed. While certain exemplary embodimentshave been described and shown in the accompanying drawings, it is to beunderstood that such embodiments are merely illustrative of and notrestrictive on other embodiments, and that such embodiments not belimited to the specific constructions and arrangements shown anddescribed, since various other modifications may occur to thoseordinarily skilled in the art upon studying this disclosure. In an areaof technology such as this, where growth is fast and furtheradvancements are not easily foreseen, the disclosed embodiments may bereadily modifiable in arrangement and detail as facilitated by enablingtechnological advancements without departing from the principles of thepresent disclosure or the scope of the accompanying claims.

What is claimed is:
 1. A processor, comprising: a front end to fetch aninstruction, the instruction to perform a calculation at a given datapoint within multi-dimensional source data using a plurality of datapoints that are adjacent to the given data point within themulti-dimensional source data in a plurality of dimensions as inputs tothe calculation; a decoder to decode the instruction; a core to, basedon the decoded instruction: perform a plurality of tabular vector readoperations, each of which is to read multiple data points of themulti-dimensional source data into a respective vector register inaccordance with a pre-determined multi-dimensional tabular vectorformat, wherein the multiple data points read by each tabular vectorread operation, collectively, have a multi-dimensional shape, whereinthe collective shape of the multiple data points within themulti-dimensional source data and the number of data points read by eachtabular vector read operation are dependent on the multi-dimensionaltabular vector format, and wherein the multiple data points read by eachtabular vector read operation include at least one of the plurality ofdata points that are adjacent to the given data point within themulti-dimensional source data and that are used as inputs to thecalculation; perform a tabular vector calculation to execute theinstruction, the tabular vector calculation based upon results ofperforming the plurality of tabular vector read operations; and writeresults of the tabular vector calculation.
 2. The processor of claim 1,wherein the instruction is a finite-difference function formatted toperform vector reads from linear vectors to tabular vectors inaccordance with the multi-dimensional tabular vector format.
 3. Theprocessor of claim 1, wherein the core is further to: store results of afirst tabular vector read operation into a first vector register; storeresults of a second tabular vector read operation into a second vectorregister, the second register and the first register having a same size;and perform another tabular vector calculation based upon results in thefirst vector register and upon results in the second vector register;wherein: the tabular vector calculation includes making a number ofcalculations in parallel; and the number of calculations is the same asthe size of the first register and the second register.
 4. The processorof claim 1, wherein the core is further to: store results of a firsttabular vector read operation into a first vector register; storeresults of a second tabular vector read operation into a second vectorregister; combine a portion of the first vector register and the secondvector register, the combined portion equal to a size of tabular vectorcalculation; and perform another tabular vector calculation based uponthe combined portions of the first vector register and the second vectorregister.
 5. The processor of claim 1, wherein the core is further to:transpose results of a first tabular vector read operation into a firstvector register; transpose results of a second tabular vector readoperation into a second vector register; and perform another tabularvector calculation based upon the first vector register and the secondvector register in another dimensional direction.
 6. The processor ofclaim 1, wherein to perform the tabular vector read operations, the coreis further to map a sufficient number of tabular vector read operationsto cover a stencil of data inputs, the stencil of a multi-dimensionalshape of the adjacent source data to calculate the data point and atleast one of the tabular vector read operations to read data inputsadjacent to each other in two or more dimensions.
 7. The processor ofclaim 1, wherein to perform the tabular vector read operations, the coreis further to map a minimum number of tabular vector read operationssufficient to cover a stencil of data inputs, the stencil of amulti-dimensional shape of the adjacent source data to calculate thedata point.
 8. A method comprising, within a processor: determining agiven data point within multi-dimensional source data at which toperform a calculation using a plurality of data points that are adjacentto the given data point within the multi-dimensional source data in aplurality of dimensions as inputs to the calculation; performing aplurality of tabular vector read operations, each of which is to readmultiple data points of the multi-dimensional source data into arespective vector register in accordance with a pre-determinedmulti-dimensional tabular vector format, wherein the multiple datapoints read by each tabular vector read operation, collectively, have amulti-dimensional shape, wherein the collective shape of the multipledata points within the multi-dimensional source data and the number ofdata points read by each tabular vector read operation are dependent onthe multi-dimensional tabular vector format, and wherein the multipledata points read by each tabular vector read operation include at leastone of the plurality of data points that are adjacent to the given datapoint within the multi-dimensional source data and that are used asinputs to the calculation; and performing a tabular vector calculationto calculate the data point, the tabular vector calculation based uponresults of performing the plurality of tabular vector read operations.9. The method of claim 8, wherein the calculation is made using afinite-difference function formatted to perform vector reads from linearvectors to tabular vectors in accordance with the multi-dimensionaltabular vector format.
 10. The method of claim 8, further comprising:storing results of a first tabular vector read operation into a firstvector register; storing results of a second tabular vector readoperation into a second vector register, the second register and thefirst register having a same size; and performing another tabular vectorcalculation based upon results m the first vector register and uponresults in the second vector register; wherein: the tabular vectorcalculation includes making a number of calculations in parallel; andthe number of calculations is the same as the size of the first registerand the second register.
 11. The method of claim 8, further comprising:storing results of a first tabular vector read operation into a firstvector register; storing results of a second tabular vector readoperation into a second vector register; combining a portion of thefirst vector register and the second vector register, the combinedportion equal to a size of tabular vector calculation; and performinganother tabular vector calculation based upon the combined portions ofthe first vector register and the second vector register.
 12. The methodof claim 8, further comprising: transposing results of a first tabularvector read operation into a first vector register; transposing resultsof a second tabular vector read operation into a second vector register;and performing another tabular vector calculation based upon the firstvector register and the second vector register in another dimensionaldirection.
 13. The method of claim 8, further comprising performing asufficient number of tabular vector read operations to cover a stencilof data inputs, the stencil of a multi-dimensional shape of the adjacentsource data to calculate the data point and at least one of the tabularvector read operations to read data inputs adjacent to each other in twoor more dimensions.
 14. A system for executing instructions, including:a front end to fetch an instruction, the instruction to perform acalculation at a given data point within multi-dimensional source datausing a plurality of data points that are adjacent to the given datapoint within the multi-dimensional source data in a plurality ofdimensions as inputs to the calculation; a decoder to decode theinstruction; a core to, based on the decoded instruction: perform aplurality of tabular vector read operations, each of which is to readmultiple data points of the multi-dimensional source data into arespective vector register in accordance with a pre-determinedmulti-dimensional tabular vector format, wherein the multiple datapoints read by each tabular vector read operation, collectively, have amulti-dimensional shape, wherein the collective shape of the multipledata points within the multi-dimensional source data and the number ofdata points read by each tabular vector read operation are dependent onthe multi-dimensional tabular vector format, and wherein the multipledata points read by each tabular vector read operation include at leastone of the plurality of data points that are adjacent to the given datapoint within the multi-dimensional source data and that are used asinputs to the calculation; perform a tabular vector calculation toexecute the instruction, the tabular vector calculation based uponresults of performing the plurality of tabular vector read operations;and write results of the tabular vector calculation.
 15. The system ofclaim 14, wherein the instruction is a finite-difference functionformatted to perform vector reads from linear vectors to tabular vectorsin accordance with the multi-dimensional tabular vector format.
 16. Thesystem of claim 14, wherein the core is further to: store results of afirst tabular vector read operation into a first vector register; storeresults of a second tabular vector read operation into a second vectorregister, the second register and the first register having a same size;and perform another tabular vector calculation based upon results in thefirst vector register and upon results in the second vector register;wherein: the tabular vector calculation includes making a number ofcalculations in parallel; and the number of calculations is the same asthe size of the first register and the second register.
 17. The systemof claim 14, wherein the core is further to: store results of a firsttabular vector read operation into a first vector register; storeresults of a second tabular vector read operation into a second vectorregister; combine a portion of the first vector register and the secondvector register, the combined portion equal to a size of tabular vectorcalculation; and perform another tabular vector calculation based uponthe combined portions of the first vector register and the second vectorregister.
 18. The system of claim 14, wherein the core is further to:transpose results of a first tabular vector read operation into a firstvector register; transpose results of a second tabular vector readoperation into a second vector register; and perform another tabularvector calculation based upon the first vector register and the secondvector register in another dimensional direction.
 19. The system ofclaim 14, wherein to perform the tabular vector read operations, thecore is further to map a sufficient number of tabular vector readoperations to cover a stencil of data inputs, the stencil of amulti-dimensional shape of the adjacent source data to calculate thedata point and at least one of the tabular vector read operations toread data inputs adjacent to each other in two or more dimensions. 20.The system of claim 14, wherein to perform the tabular vector readoperations, the core is further to map a minimum number of tabularvector read operations sufficient to cover a stencil of data inputs, thestencil of a multi-dimensional shape of the adjacent source data tocalculate the data point.